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Sökning: WFRF:(Ulén Johannes)

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1.
  • Abuhasanein, Suleiman, et al. (författare)
  • A novel model of artificial intelligence based automated image analysis of CT urography to identify bladder cancer in patients investigated for macroscopic hematuria
  • 2024
  • Ingår i: Scandinavian journal of urology. - : Medical Journal Sweden AB. - 2168-1805 .- 2168-1813. ; 59, s. 90-97
  • Tidskriftsartikel (refereegranskat)abstract
    • Objective: To evaluate whether artificial intelligence (AI) based automatic image analysis utilising convolutional neural networks (CNNs) can be used to evaluate computed tomography urography (CTU) for the presence of urinary bladder cancer (UBC) in patients with macroscopic hematuria. Methods: Our study included patients who had undergone evaluation for macroscopic hematuria. A CNN-based AI model was trained and validated on the CTUs included in the study on a dedicated research platform (Recomia.org). Sensitivity and specificity were calculated to assess the performance of the AI model. Cystoscopy findings were used as the reference method. Results: The training cohort comprised a total of 530 patients. Following the optimisation process, we developed the last version of our AI model. Subsequently, we utilised the model in the validation cohort which included an additional 400 patients (including 239 patients with UBC). The AI model had a sensitivity of 0.83 (95% confidence intervals [CI], 0.76-0.89), specificity of 0.76 (95% CI 0.67-0.84), and a negative predictive value (NPV) of 0.97 (95% CI 0.95-0.98). The majority of tumours in the false negative group (n = 24) were solitary (67%) and smaller than 1 cm (50%), with the majority of patients having cTaG1-2 (71%). Conclusions: We developed and tested an AI model for automatic image analysis of CTUs to detect UBC in patients with macroscopic hematuria. This model showed promising results with a high detection rate and excessive NPV. Further developments could lead to a decreased need for invasive investigations and prioritising patients with serious tumours.
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2.
  • Abuhasanein, Suleiman, et al. (författare)
  • A novel model of artificial intelligence based automated image analysis of CT urography to identify bladder cancer in patients investigated for macroscopic hematuria
  • 2024
  • Ingår i: Scandinavian Journal of Urology. - : Medical Journal Sweden AB. - 2168-1805 .- 2168-1813. ; 59, s. 90-97
  • Tidskriftsartikel (refereegranskat)abstract
    • OBJECTIVE: To evaluate whether artificial intelligence (AI) based automatic image analysis utilising convolutional neural networks (CNNs) can be used to evaluate computed tomography urography (CTU) for the presence of urinary bladder cancer (UBC) in patients with macroscopic hematuria. METHODS: Our study included patients who had undergone evaluation for macroscopic hematuria. A CNN-based AI model was trained and validated on the CTUs included in the study on a dedicated research platform (Recomia.org). Sensitivity and specificity were calculated to assess the performance of the AI model. Cystoscopy findings were used as the reference method. RESULTS: The training cohort comprised a total of 530 patients. Following the optimisation process, we developed the last version of our AI model. Subsequently, we utilised the model in the validation cohort which included an additional 400 patients (including 239 patients with UBC). The AI model had a sensitivity of 0.83 (95% confidence intervals [CI], 0.76-0.89), specificity of 0.76 (95% CI 0.67-0.84), and a negative predictive value (NPV) of 0.97 (95% CI 0.95-0.98). The majority of tumours in the false negative group (n = 24) were solitary (67%) and smaller than 1 cm (50%), with the majority of patients having cTaG1-2 (71%). CONCLUSIONS: We developed and tested an AI model for automatic image analysis of CTUs to detect UBC in patients with macroscopic hematuria. This model showed promising results with a high detection rate and excessive NPV. Further developments could lead to a decreased need for invasive investigations and prioritising patients with serious tumours.
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3.
  • Alvén, Jennifer, 1989, et al. (författare)
  • Shape-aware label fusion for multi-atlas frameworks
  • 2019
  • Ingår i: Pattern Recognition Letters. - : Elsevier BV. - 0167-8655. ; 124, s. 109-117
  • Tidskriftsartikel (refereegranskat)abstract
    • Despite of having no explicit shape model, multi-atlas approaches to image segmentation have proved to be a top-performer for several diverse datasets and imaging modalities. In this paper, we show how one can directly incorporate shape regularization into the multi-atlas framework. Unlike traditional multi-atlas methods, our proposed approach does not rely on label fusion on the voxel level. Instead, each registered atlas is viewed as an estimate of the position of a shape model. We evaluate and compare our method on two public benchmarks: (i) the VISCERAL Grand Challenge on multi-organ segmentation of whole-body CT images and (ii) the Hammers brain atlas of MR images for segmenting the hippocampus and the amygdala. For this wide spectrum of both easy and hard segmentation tasks, our experimental quantitative results are on par or better than state-of-the-art. More importantly, we obtain qualitatively better segmentation boundaries, for instance, preserving topology and fine structures.
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4.
  • Alvén, Jennifer, 1989, et al. (författare)
  • Shape-aware multi-atlas segmentation
  • 2016
  • Ingår i: Proceedings - International Conference on Pattern Recognition. - 1051-4651. ; 0, s. 1101-1106
  • Konferensbidrag (refereegranskat)abstract
    • Despite of having no explicit shape model, multi-atlas approaches to image segmentation have proved to be a top-performer for several diverse datasets and imaging modalities. In this paper, we show how one can directly incorporate shape regularization into the multi-atlas framework. Unlike traditional methods, our proposed approach does not rely on label fusion on the voxel level. Instead, each registered atlas is viewed as an estimate of the position of a shape model. We evaluate and compare our method on two public benchmarks: (i) the VISCERAL Grand Challenge on multi-organ segmentation of whole-body CT images and (ii) the Hammers brain atlas of MR images for segmenting the hippocampus and the amygdala. For this wide spectrum of both easy and hard segmentation tasks, our experimental quantitative results are on par or better than state-of-the-art. More importantly, we obtain qualitatively better segmentation boundaries, for instance, preserving fine structures.
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5.
  • Borrelli, P., et al. (författare)
  • AI-based detection of lung lesions in F-18 FDG PET-CT from lung cancer patients
  • 2021
  • Ingår i: Ejnmmi Physics. - : Springer Science and Business Media LLC. - 2197-7364. ; 8:1
  • Tidskriftsartikel (refereegranskat)abstract
    • Background[F-18]-fluorodeoxyglucose (FDG) positron emission tomography with computed tomography (PET-CT) is a well-established modality in the work-up of patients with suspected or confirmed diagnosis of lung cancer. Recent research efforts have focused on extracting theragnostic and textural information from manually indicated lung lesions. Both semi-automatic and fully automatic use of artificial intelligence (AI) to localise and classify FDG-avid foci has been demonstrated. To fully harness AI's usefulness, we have developed a method which both automatically detects abnormal lung lesions and calculates the total lesion glycolysis (TLG) on FDG PET-CT.MethodsOne hundred twelve patients (59 females and 53 males) who underwent FDG PET-CT due to suspected or for the management of known lung cancer were studied retrospectively. These patients were divided into a training group (59%; n = 66), a validation group (20.5%; n = 23) and a test group (20.5%; n = 23). A nuclear medicine physician manually segmented abnormal lung lesions with increased FDG-uptake in all PET-CT studies. The AI-based method was trained to segment the lesions based on the manual segmentations. TLG was then calculated from manual and AI-based measurements, respectively and analysed with Bland-Altman plots.ResultsThe AI-tool's performance in detecting lesions had a sensitivity of 90%. One small lesion was missed in two patients, respectively, where both had a larger lesion which was correctly detected. The positive and negative predictive values were 88% and 100%, respectively. The correlation between manual and AI TLG measurements was strong (R-2 = 0.74). Bias was 42 g and 95% limits of agreement ranged from -736 to 819 g. Agreement was particularly high in smaller lesions.ConclusionsThe AI-based method is suitable for the detection of lung lesions and automatic calculation of TLG in small- to medium-sized tumours. In a clinical setting, it will have an added value due to its capability to sort out negative examinations resulting in prioritised and focused care on patients with potentially malignant lesions.
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  • Borrelli, P., et al. (författare)
  • Artificial intelligence-aided CT segmentation for body composition analysis: a validation study
  • 2021
  • Ingår i: European Radiology Experimental. - : Springer Science and Business Media LLC. - 2509-9280. ; 5:1
  • Tidskriftsartikel (refereegranskat)abstract
    • BackgroundBody composition is associated with survival outcome in oncological patients, but it is not routinely calculated. Manual segmentation of subcutaneous adipose tissue (SAT) and muscle is time-consuming and therefore limited to a single CT slice. Our goal was to develop an artificial-intelligence (AI)-based method for automated quantification of three-dimensional SAT and muscle volumes from CT images.MethodsEthical approvals from Gothenburg and Lund Universities were obtained. Convolutional neural networks were trained to segment SAT and muscle using manual segmentations on CT images from a training group of 50 patients. The method was applied to a separate test group of 74 cancer patients, who had two CT studies each with a median interval between the studies of 3days. Manual segmentations in a single CT slice were used for comparison. The accuracy was measured as overlap between the automated and manual segmentations.ResultsThe accuracy of the AI method was 0.96 for SAT and 0.94 for muscle. The average differences in volumes were significantly lower than the corresponding differences in areas in a single CT slice: 1.8% versus 5.0% (p <0.001) for SAT and 1.9% versus 3.9% (p < 0.001) for muscle. The 95% confidence intervals for predicted volumes in an individual subject from the corresponding single CT slice areas were in the order of 20%.Conclusions The AI-based tool for quantification of SAT and muscle volumes showed high accuracy and reproducibility and provided a body composition analysis that is more relevant than manual analysis of a single CT slice.
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9.
  • Borrelli, Pablo, et al. (författare)
  • Artificial intelligence-based detection of lymph node metastases by PET/CT predicts prostate cancer-specific survival
  • 2021
  • Ingår i: Clinical Physiology and Functional Imaging. - : Wiley. - 1475-0961 .- 1475-097X. ; 41:1, s. 62-67
  • Tidskriftsartikel (refereegranskat)abstract
    • Introduction Lymph node metastases are a key prognostic factor in prostate cancer (PCa), but detecting lymph node lesions from PET/CT images is a subjective process resulting in inter-reader variability. Artificial intelligence (AI)-based methods can provide an objective image analysis. We aimed at developing and validating an AI-based tool for detection of lymph node lesions. Methods A group of 399 patients with biopsy-proven PCa who had undergone(18)F-choline PET/CT for staging prior to treatment were used to train (n = 319) and test (n = 80) the AI-based tool. The tool consisted of convolutional neural networks using complete PET/CT scans as inputs. In the test set, the AI-based lymph node detections were compared to those of two independent readers. The association with PCa-specific survival was investigated. Results The AI-based tool detected more lymph node lesions than Reader B (98 vs. 87/117;p = .045) using Reader A as reference. AI-based tool and Reader A showed similar performance (90 vs. 87/111;p = .63) using Reader B as reference. The number of lymph node lesions detected by the AI-based tool, PSA, and curative treatment was significantly associated with PCa-specific survival. Conclusion This study shows the feasibility of using an AI-based tool for automated and objective interpretation of PET/CT images that can provide assessments of lymph node lesions comparable with that of experienced readers and prognostic information in PCa patients.
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12.
  • Borrelli, P., et al. (författare)
  • Freely available convolutional neural network-based quantification of PET/CT lesions is associated with survival in patients with lung cancer
  • 2022
  • Ingår i: EJNMMI Physics. - : Springer Science and Business Media LLC. - 2197-7364. ; 9:1
  • Tidskriftsartikel (refereegranskat)abstract
    • Background: Metabolic positron emission tomography/computed tomography (PET/CT) parameters describing tumour activity contain valuable prognostic information, but to perform the measurements manually leads to both intra- and inter-reader variability and is too time-consuming in clinical practice. The use of modern artificial intelligence-based methods offers new possibilities for automated and objective image analysis of PET/CT data. Purpose: We aimed to train a convolutional neural network (CNN) to segment and quantify tumour burden in [18F]-fluorodeoxyglucose (FDG) PET/CT images and to evaluate the association between CNN-based measurements and overall survival (OS) in patients with lung cancer. A secondary aim was to make the method available to other researchers. Methods: A total of 320 consecutive patients referred for FDG PET/CT due to suspected lung cancer were retrospectively selected for this study. Two nuclear medicine specialists manually segmented abnormal FDG uptake in all of the PET/CT studies. One-third of the patients were assigned to a test group. Survival data were collected for this group. The CNN was trained to segment lung tumours and thoracic lymph nodes. Total lesion glycolysis (TLG) was calculated from the CNN-based and manual segmentations. Associations between TLG and OS were investigated using a univariate Cox proportional hazards regression model. Results: The test group comprised 106 patients (median age, 76years (IQR 61–79); n = 59 female). Both CNN-based TLG (hazard ratio 1.64, 95% confidence interval 1.21–2.21; p = 0.001) and manual TLG (hazard ratio 1.54, 95% confidence interval 1.14–2.07; p = 0.004) estimations were significantly associated with OS. Conclusion: Fully automated CNN-based TLG measurements of PET/CT data showed were significantly associated with OS in patients with lung cancer. This type of measurement may be of value for the management of future patients with lung cancer. The CNN is publicly available for research purposes. © 2022, The Author(s).
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13.
  • Bölscher, Tobias, et al. (författare)
  • Changes in pore networks and readily dispersible soil following structure liming of clay soils
  • 2021
  • Ingår i: Geoderma. - : Elsevier BV. - 0016-7061 .- 1872-6259. ; 390
  • Tidskriftsartikel (refereegranskat)abstract
    • Structure liming aims to improve soil structure (i.e., the spatial arrangement of particles and pores) and its stability against external and internal forces. Effects of lime application on soil structure have received considerable interest, but only a few studies have investigated effects on macro- and mesopore networks. We used X-ray computed tomography to image macropore networks (ø ≥ 0.3 mm) in soil columns and mesopores (ø ≥ 0.01 mm) in soil aggregates from three field sites with (silty) clay soils after the application of structure lime (3.1 t ha−1 or 5 t ha−1 of CaO equivalent). Segmented X-ray images were used to quantify soil porosity and pore size distributions as well as to analyse pore architecture and connectivity metrics. In addition, we investigated the amount of readily dispersible soil particles. Our results demonstrate that structure liming affected both, macropore networks and amounts of readily dispersible soil to different degrees, depending on the field site. Significant changes in macropore networks and amounts of readily dispersible soil after lime application were found for one of the three field sites, while only some indications for similar changes were observed at the other two sites. Overall, structure liming tended to decrease soil macroporosity and shift pore size distribution from larger (ε>1.0 mm) and medium sized macropores (ε0.3–1.0 mm) towards smaller macropores (ε0.1–0.3 mm). Furthermore, liming tended to decrease the critical and average pore diameters, while increasing the surface fractal dimension and specific surface area of macropore network. Structure liming also reduced the amounts of readily dispersible soil particles. We did not find any changes in mesopore network properties within soil aggregates or biopore networks in columns and aggregates. The effects of lime on macropore networks remain elusive, but may be caused by the formation of hydrate phases and carbonates which occupy pore space.
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14.
  • Fejne, Frida, 1986, et al. (författare)
  • Multiatlas Segmentation Using Robust Feature-Based Registration
  • 2017
  • Ingår i: , Cloud-Based Benchmarking of Medical Image Analysis. - Cham : Springer International Publishing. - 9783319496429 ; , s. 203-218
  • Bokkapitel (övrigt vetenskapligt/konstnärligt)abstract
    • This paper presents a pipeline which uses a multiatlas approach for multiorgan segmentation in whole-body CT images. In order to obtain accurate registrations between the target and the atlas images, we develop an adapted feature-based method which uses organ-specific features. These features are learnt during an offline preprocessing step, and thus, the algorithm still benefits from the speed of feature-based registration methods. These feature sets are then used to obtain pairwise non-rigid transformations using RANSAC followed by a thin-plate spline refinement or NiftyReg. The fusion of the transferred atlas labels is performed using a random forest classifier, and finally, the segmentation is obtained using graph cuts with a Potts model as interaction term. Our pipeline was evaluated on 20 organs in 10 whole-body CT images at the VISCERAL Anatomy Challenge, in conjunction with the International Symposium on Biomedical Imaging, Brooklyn, New York, in April 2015. It performed best on majority of the organs, with respect to the Dice index.
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15.
  • Hellner, Qarin, et al. (författare)
  • Effects of tillage and liming on macropore networks derived from X-ray tomography images of a silty clay soil
  • 2018
  • Ingår i: Soil Use and Management. - : Wiley. - 0266-0032 .- 1475-2743. ; 34, s. 197-205
  • Tidskriftsartikel (refereegranskat)abstract
    • Soil structure influences water infiltration, aeration and root growth and, thereby, also the conditions for sustainable crop production. Our objective was to quantify the effects of different soil management methods and land uses on the topsoil structure of a silty clay soil. We sampled 32 intact soil columns (18 cm high, 12.7 cm diameter) from an experimental silty clay field with four treatments: conventional tillage (CT), conventional tillage followed by liming (CTL), reduced tillage (RT) and unfertilized fallow (UF). The columns were analysed using 3-D X-ray tomography. The samples were taken in autumn after harvest, 7 yr after quick lime was applied to the CTL plots. Despite a relatively large number of replicates per treatment (8, 8, 8 and 6 (two UF samples were excluded), respectively), there were no significant differences between any of the investigated macropore network properties related to tilled treatments. The UF treatment, in contrast, exhibited more vertically oriented macropores, which were also better connected compared to the other treatments. This confirms previous findings that tillage may disrupt the vertical continuity of macropore clusters. The impact of liming on soil pore network properties may have been limited to pores smaller than the resolution in our X-ray images. It is also possible that the effects of lime on soil structure were limited to a few years which means that any effect would have diminished by the time of this study. These matters should be further investigated in follow-up studies to understand better the potential of lime amendments to clay soil.
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16.
  • Kaboteh, Reza, et al. (författare)
  • Convolutional neural network based quantification of choline uptake in PET/CT studies is associated with overall survival in patients with prostate cancer
  • 2017
  • Ingår i: European Journal of Nuclear Medicine and Molecular Imaging. - 1619-7070 .- 1619-7089. ; 44:supplement 2
  • Tidskriftsartikel (refereegranskat)abstract
    • Aim : To develop a convolutional neural network (CNN) based automated method for quantification of 18F-choline uptake in the prostate gland in PET/CT studies and to study the association between this measure, clinical data and overall survival in patients with prostate cancer. Methods : A CNN was trained to segment the prostate gland in CT images using manual segmentations performed by a radiologist in a group of 100 patients, who had undergone 18F-FDG PET/CT. After the training process, the CNN automatically segmented the prostate gland in the CT images and SUV values in the corresponding PET images were automatically analyzed in a separate validation group consisting of 45 patients with biopsy-proven hormone-naïve prostate cancer. All patients had undergone an 18F-choline PET/CT as part of a previous research project. Voxels localized in the prostate gland and having a SUV >2.65 were defined as abnormal, as proposed by Reske S et al. (2006). Automated calculation of the following five PET measurements was performed: maximal SUV within the prostate gland - SUVmax; average SUV for voxels with SUV >2.65 - SUVmean; volume of voxels with SUV >2.65 - VOL; fraction of VOL related to the whole volume of the prostate gland - FRAC; product SUVmean x FRAC defined as Total Lesion Uptake - TLU. The association between the automated PET measurements, age, PSA, Gleason score and overall survival (OS) was evaluated using a univariate Cox proportional hazards regression model. Kaplan-Meier analysis was used to estimate the survival difference (log-rank test). Results : TLU and FRAC were significantly associated with OS in the Cox analysis while the other three PET measurements; age, PSA and Gleason score were not. Kaplan-Meier analysis showed that patients with SUVmax <5.3, SUVmean <3.5 and TLU <1 showed significantly longer survival times than patients with values higher than these thresholds. No significant differences were found when patients were stratified based on the other two PET measurements, PSA or Gleason score. Conclusion : Measurements reflecting 18F-choline PET uptake in the prostate gland obtained using a completely automated method were significantly associated with OS in patients with hormone-naïve prostate cancer. This type of objective quantification of PET/CT studies could be of value also for other PET tracers and other cancers in the future.
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17.
  • Kahl, Fredrik, 1972, et al. (författare)
  • Good Features for Reliable Registration in Multi-Atlas Segmentation
  • 2015
  • Ingår i: CEUR Workshop Proceedings. - 1613-0073. ; 1390:January, s. 12-17
  • Konferensbidrag (refereegranskat)abstract
    • This work presents a method for multi-organ segmentation in whole-body CT images based on a multi-atlas approach. A robust and efficient feature-based registration technique is developed which uses sparse organ specific features that are learnt based on their ability to register different organ types accurately. The best fitted feature points are used in RANSAC to estimate an affine transformation, followed by a thin plate spline refinement. This yields an accurate and reliable nonrigid transformation for each organ, which is independent of initialization and hence does not suffer from the local minima problem. Further, this is accomplished at a fraction of the time required by intensity-based methods. The technique is embedded into a standard multi-atlas framework using label transfer and fusion, followed by a random forest classifier which produces the data term for the final graph cut segmentation. For a majority of the classes our approach outperforms the competitors at the VISCERAL Anatomy Grand Challenge on segmentation at ISBI 2015.
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18.
  • Lind, Erica, et al. (författare)
  • Automated quantification of reference levels in liver and mediastinum (blood pool) for the Deauville therapy response classification using FDG-PET/CT in lymphoma patients
  • 2017
  • Ingår i: European Journal of Nuclear Medicine and Molecular Imaging. - 1619-7070 .- 1619-7089. ; 44:supplement 2
  • Tidskriftsartikel (refereegranskat)abstract
    • Aim : To develop and validate a convolutional neural network (CNN) based method for automated quantification of reference levels in liver and mediastinum (blood pool) for the Deauville therapy response classification using FDG-PET/CT in lymphoma patients. Methods : CNNs were trained to segment the liver and the mediastinum, defined as the thoracic part of the aorta, in CT images from 81 consecutive lymphoma patients, who had undergone FDG-PET/CT examinations. Trained image readers segmented the liver and aorta manually in each of the CT images and these segmentations together with the CT images were used to train the CNN. After the training process, the CNN method was applied to a separate validation group consisting of six consecutive lymphoma patients (17-82 years, 3 female). First, the liver and mediastinum were automatically segmented in the CT images. Second, voxels in the corresponding FDG-PET images, which were localized in the liver and mediastinum, were selected and the median standard uptake value (SUV) was calculated. The CNN based analysis was compared to corresponding manual segmentations by two experienced radiologists. The Dice index was used to analyse the overlap between the segmentations by the CNN and the two radiologists. A Dice index of 1.00 indicates perfect matching. Results : The mean Dice indices for the comparison between CNN based liver segmentations and those of the two radiologists in the validation group were 0.95 and 0.95. A corresponding comparison between the two radiologists also resulted in a Dice index of 0.95. The mean liver volumes were 1,752ml, 1,757ml and 1,768ml for the CNN and two radiologists, respectively. The median SUV for the liver was on average 1.8 and the differences between median SUV based on CNN and manual segmentations were less or equal to 0.1. The mean Dice indices for the mediastinum were 0.80, 0.83 (CNN vs radiologists) and 0.86 (comparing the two radiologists). The mean mediastinum (aorta) volumes were 147ml, 140ml and 125ml for the CNN and two radiologists, respectively. The median SUV for the mediastinum was on average 1.4 and the differences between median SUV based on CNN and manual segmentations were less or equal to 0.14. Conclusion : A CNN based method for automated quantification of reference levels in liver and mediastinum show good agreement with results obtained by experienced radiologists, who manually segmented the CT images. This is a first and promising step towards a completely objective treatment response evaluation in patients with lymphoma based on FDG-PET/CT.
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  • Lindgren Belal, Sarah, et al. (författare)
  • Deep learning-based evaluation of normal bone marrow activity in 18F-NaF PET/CT in patients with prostate cancer
  • 2020
  • Ingår i: Insights into Imaging. - : Springer Science and Business Media LLC. - 1869-4101. ; 11:Suppl. 1, s. 349-350
  • Konferensbidrag (refereegranskat)abstract
    • Purpose: Bone marrow is the primary site of skeletal metastases in prostate cancer. 18F-sodium fluoride (NaF) can be used to detect malignant activity, but also identifies irrelevant degenerative cortical uptake. Normal radiotracer activity in solely the marrow has yet to be described and could be a first step towards automated tumor burden calculation as SUV thresholds. We aimed to investigate normal activity of 18F-NaF in whole bone and bone marrow in patients with localized prostate cancer.Methods and materials: 18F-NaF PET/CT scans from 87 patients with high-risk prostate cancer from two centers were retrospectively analyzed. All patients had a recent negative or inconclusive bone scan. In the first center, PET scan was acquired 1-1.5 hours after i.v. injection of 4 MBq/kg 18F-NaF on an integrated PET/CT system (Gemini TF, Philips Medical Systems) (53/87). In the second center, scanning was performed 1 hour after i.v. injection of 3 MBq/kg 18F-NaF on an integrated PET/CT system (Discovery ST, GE Healthcare) (34/87). CT scans were obtained in immediate connection to the PET scan. Automated segmentations of vertebrae, pelvis, femora, humeri and sternum were performed in the CT scans using a deep learning-based method. Bone <7 mm from skeletal surfaces was removed to isolate the marrow. SUV was measured within the remaining area in the PET scan.Results: SUVmax and SUVmean in the whole bone and bone marrow of the different regions were presented.Conclusion: We present a deep-learning approach for evaluation of normal radiotracer activity in whole bone and bone marrow. Knowledge about radiotracer uptake in the normal bone prior to cancerous involvement is a necessary first step for subsequent tumor assessment and could be of value in the implementation of future tracers.
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20.
  • Lindgren Belal, Sarah, et al. (författare)
  • Deep learning for segmentation of 49 selected bones in CT scans: First step in automated PET/CT-based 3D quantification of skeletal metastases
  • 2019
  • Ingår i: European Journal of Radiology. - : Elsevier BV. - 0720-048X .- 1872-7727. ; 113, s. 89-95
  • Tidskriftsartikel (refereegranskat)abstract
    • Purpose: The aim of this study was to develop a deep learning-based method for segmentation of bones in CT scans and test its accuracy compared to manual delineation, as a first step in the creation of an automated PET/CT-based method for quantifying skeletal tumour burden. Methods: Convolutional neural networks (CNNs) were trained to segment 49 bones using manual segmentations from 100 CT scans. After training, the CNN-based segmentation method was tested on 46 patients with prostate cancer, who had undergone 18 F-choline-PET/CT and 18 F-NaF PET/CT less than three weeks apart. Bone volumes were calculated from the segmentations. The network's performance was compared with manual segmentations of five bones made by an experienced physician. Accuracy of the spatial overlap between automated CNN-based and manual segmentations of these five bones was assessed using the Sørensen-Dice index (SDI). Reproducibility was evaluated applying the Bland-Altman method. Results: The median (SD) volumes of the five selected bones were by CNN and manual segmentation: Th7 41 (3.8) and 36 (5.1), L3 76 (13) and 75 (9.2), sacrum 284 (40) and 283 (26), 7th rib 33 (3.9) and 31 (4.8), sternum 80 (11) and 72 (9.2), respectively. Median SDIs were 0.86 (Th7), 0.85 (L3), 0.88 (sacrum), 0.84 (7th rib) and 0.83 (sternum). The intraobserver volume difference was less with CNN-based than manual approach: Th7 2% and 14%, L3 7% and 8%, sacrum 1% and 3%, 7th rib 1% and 6%, sternum 3% and 5%, respectively. The average volume difference measured as ratio volume difference/mean volume between the two CNN-based segmentations was 5–6% for the vertebral column and ribs and ≤3% for other bones. Conclusion: The new deep learning-based method for automated segmentation of bones in CT scans provided highly accurate bone volumes in a fast and automated way and, thus, appears to be a valuable first step in the development of a clinical useful processing procedure providing reliable skeletal segmentation as a key part of quantification of skeletal metastases.
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21.
  • Molnar, David, et al. (författare)
  • Artificial intelligence based automatic quantification of epicardial adipose tissue suitable for large scale population studies
  • 2021
  • Ingår i: Scientific Reports. - : Springer Science and Business Media LLC. - 2045-2322 .- 2045-2322. ; 11:1
  • Tidskriftsartikel (refereegranskat)abstract
    • To develop a fully automatic model capable of reliably quantifying epicardial adipose tissue (EAT) volumes and attenuation in large scale population studies to investigate their relation to markers of cardiometabolic risk. Non-contrast cardiac CT images from the SCAPIS study were used to train and test a convolutional neural network based model to quantify EAT by: segmenting the pericardium, suppressing noise-induced artifacts in the heart chambers, and, if image sets were incomplete, imputing missing EAT volumes. The model achieved a mean Dice coefficient of 0.90 when tested against expert manual segmentations on 25 image sets. Tested on 1400 image sets, the model successfully segmented 99.4% of the cases. Automatic imputation of missing EAT volumes had an error of less than 3.1% with up to 20% of the slices in image sets missing. The most important predictors of EAT volumes were weight and waist, while EAT attenuation was predicted mainly by EAT volume. A model with excellent performance, capable of fully automatic handling of the most common challenges in large scale EAT quantification has been developed. In studies of the importance of EAT in disease development, the strong co-variation with anthropometric measures needs to be carefully considered.
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22.
  • Mortensen, Mike A., et al. (författare)
  • Artificial intelligence-based versus manual assessment of prostate cancer in the prostate gland: a method comparison study
  • 2019
  • Ingår i: Clinical Physiology and Functional Imaging. - : Wiley. - 1475-0961 .- 1475-097X. ; 39:6, s. 399-406
  • Tidskriftsartikel (refereegranskat)abstract
    • Aim : To test the feasibility of a fully automated artificial intelligence-based method providing PET measures of prostate cancer (PCa). Methods : A convolutional neural network (CNN) was trained for automated measurements in 18F-choline (FCH) PET/CT scans obtained prior to radical prostatectomy (RP) in 45 patients with newly diagnosed PCa. Automated values were obtained for prostate volume, maximal standardized uptake value (SUVmax), mean standardized uptake value of voxels considered abnormal (SUVmean) and volume of abnormal voxels (Volabn). The product SUVmean × Volabn was calculated to reflect total lesion uptake (TLU). Corresponding manual measurements were performed. CNN-estimated data were compared with the weighted surgically removed tissue specimens and manually derived data and related to clinical parameters assuming that 1 g ≈ 1 ml of tissue. Results : The mean (range) weight of the prostate specimens was 44 g (20–109), while CNN-estimated volume was 62 ml (31–108) with a mean difference of 13·5 g or ml (95% CI: 9·78–17·32). The two measures were significantly correlated (r = 0·77, P<0·001). Mean differences (95% CI) between CNN-based and manually derived PET measures of SUVmax, SUVmean, Volabn (ml) and TLU were 0·37 (−0·01 to 0·75), −0·08 (−0·30 to 0·14), 1·40 (−2·26 to 5·06) and 9·61 (−3·95 to 23·17), respectively. PET findings Volabn and TLU correlated with PSA (P<0·05), but not with Gleason score or stage. Conclusion : Automated CNN segmentation provided in seconds volume and simple PET measures similar to manually derived ones. Further studies on automated CNN segmentation with newer tracers such as radiolabelled prostate-specific membrane antigen are warranted.
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23.
  • Olsson, Carl, et al. (författare)
  • In Defense of 3D-Label Stereo
  • 2013
  • Ingår i: Computer Vision and Pattern Recognition (CVPR), 2013 IEEE Conference on. - 1063-6919 .- 2163-6648. ; , s. 1730-1737
  • Konferensbidrag (refereegranskat)abstract
    • It is commonly believed that higher order smoothness should be modeled using higher order interactions. For example, 2nd order derivatives for deformable (active) contours are represented by triple cliques. Similarly, the 2nd order regularization methods in stereo predominantly use MRF models with scalar (1D) disparity labels and triple clique interactions. In this paper we advocate a largely overlooked alternative approach to stereo where 2nd order surface smoothness is represented by pairwise interactions with 3D-labels, e.g. tangent planes. This general paradigm has been criticized due to perceived computational complexity of optimization in higher-dimensional label space. Contrary to popular beliefs, we demonstrate that representing 2nd order surface smoothness with 3D labels leads to simpler optimization problems with (nearly) submodular pairwise interactions. Our theoretical and experimental results demonstrate advantages over state-of-the-art methods for 2nd order smoothness stereo.
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24.
  • Olsson, Carl, et al. (författare)
  • Local Refinement for Stereo Regularization
  • 2014
  • Ingår i: Pattern Recognition (ICPR), 2014 22nd International Conference on. - 1051-4651. ; , s. 4056-4061
  • Konferensbidrag (refereegranskat)abstract
    • Stereo matching is an inherently difficult problem due to ambiguous and noisy texture. The non-convexity and non- differentiability makes local linear (or quadratic) approximations poor, thereby preventing the use of standard local descent methods. Therefore recent methods are predominantly based on discretization and/or random sampling of some class of approximating surfaces (e.g. planes). While these methods are very efficient in generating a rough surface estimate, via either fusion of proposals or label propagation, the end result is usually not as smooth as desired. In this paper we show that, if the objective function is decomposed correctly, local refinement of candidate solutions can be performed using an ADMM approach. This allows searching over more general function classes, thereby resulting in visually more appealing smooth surface estimations.
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25.
  • Olsson, Carl, et al. (författare)
  • Partial Enumeration and Curvature Regularization
  • 2013
  • Ingår i: Computer Vision (ICCV), 2013 IEEE International Conference on. - 1550-5499. ; , s. 2936-2943
  • Konferensbidrag (refereegranskat)abstract
    • Energies with high-order non-submodular interactions have been shown to be very useful in vision due to their high modeling power. Optimization of such energies, however, is generally NP-hard. A naive approach that works for small problem instances is exhaustive search, that is, enumera- tion of all possible labelings of the underlying graph. We propose a general minimization approach for large graphs based on enumeration of labelings of certain small patches. This partial enumeration technique reduces complex high- order energy formulations to pairwise Constraint Satisfac- tion Problems with unary costs (uCSP), which can be ef- ficiently solved using standard methods like TRW-S. Our approach outperforms a number of existing state-of-the-art algorithms on well known difficult problems (e.g. curvature regularization, stereo, deconvolution); it gives near global minimum and better speed. Our main application of interest is curvature regular- ization. In the context of segmentation, our partial enu- meration technique allows to evaluate curvature directly on small patches using a novel integral geometry approach.
  •  
26.
  • Polymeri, Erini, et al. (författare)
  • Artificial Intelligence-Based Organ Delineation for Radiation Treatment Planning of Prostate Cancer on Computed Tomography
  • 2024
  • Ingår i: Advances in Radiation Oncology. - 2452-1094. ; 9:3
  • Tidskriftsartikel (refereegranskat)abstract
    • Purpose: Meticulous manual delineations of the prostate and the surrounding organs at risk are necessary for prostate cancer radiation therapy to avoid side effects to the latter. This process is time consuming and hampered by inter- and intraobserver variability, all of which could be alleviated by artificial intelligence (AI). This study aimed to evaluate the performance of AI compared with manual organ delineations on computed tomography (CT) scans for radiation treatment planning. Methods and Materials: Manual delineations of the prostate, urinary bladder, and rectum of 1530 patients with prostate cancer who received curative radiation therapy from 2006 to 2018 were included. Approximately 50% of those CT scans were used as a training set, 25% as a validation set, and 25% as a test set. Patients with hip prostheses were excluded because of metal artifacts. After training and fine-tuning with the validation set, automated delineations of the prostate and organs at risk were obtained for the test set. Sørensen-Dice similarity coefficient, mean surface distance, and Hausdorff distance were used to evaluate the agreement between the manual and automated delineations. Results: The median Sørensen-Dice similarity coefficient between the manual and AI delineations was 0.82, 0.95, and 0.88 for the prostate, urinary bladder, and rectum, respectively. The median mean surface distance and Hausdorff distance were 1.7 and 9.2 mm for the prostate, 0.7 and 6.7 mm for the urinary bladder, and 1.1 and 13.5 mm for the rectum, respectively. Conclusions: Automated CT-based organ delineation for prostate cancer radiation treatment planning is feasible and shows good agreement with manually performed contouring.
  •  
27.
  • Polymeri, Erini, et al. (författare)
  • Deep learning-based quantification of PET/CT prostate gland uptake : association with overall survival
  • 2020
  • Ingår i: Clinical Physiology and Functional Imaging. - Chichester : Blackwell Publishing. - 1475-0961 .- 1475-097X. ; 40:2, s. 106-113
  • Tidskriftsartikel (refereegranskat)abstract
    • Aim: To validate a deep-learning (DL) algorithm for automated quantification of prostate cancer on positron emission tomography/computed tomography (PET/CT) and explore the potential of PET/CT measurements as prognostic biomarkers. Material and methods: Training of the DL-algorithm regarding prostate volume was performed on manually segmented CT images in 100 patients. Validation of the DL-algorithm was carried out in 45 patients with biopsy-proven hormone-naïve prostate cancer. The automated measurements of prostate volume were compared with manual measurements made independently by two observers. PET/CT measurements of tumour burden based on volume and SUV of abnormal voxels were calculated automatically. Voxels in the co-registered 18F-choline PET images above a standardized uptake value (SUV) of 2·65, and corresponding to the prostate as defined by the automated segmentation in the CT images, were defined as abnormal. Validation of abnormal voxels was performed by manual segmentation of radiotracer uptake. Agreement between algorithm and observers regarding prostate volume was analysed by Sørensen-Dice index (SDI). Associations between automatically based PET/CT biomarkers and age, prostate-specific antigen (PSA), Gleason score as well as overall survival were evaluated by a univariate Cox regression model. Results: The SDI between the automated and the manual volume segmentations was 0·78 and 0·79, respectively. Automated PET/CT measures reflecting total lesion uptake and the relation between volume of abnormal voxels and total prostate volume were significantly associated with overall survival (P = 0·02), whereas age, PSA, and Gleason score were not. Conclusion: Automated PET/CT biomarkers showed good agreement to manual measurements and were significantly associated with overall survival. © 2019 The Authors. Clinical Physiology and Functional Imaging published by John Wiley & Sons Ltd on behalf of Scandinavian Society of Clinical Physiology and Nuclear Medicine
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28.
  • Sachpekidis, Christos, et al. (författare)
  • Artificial intelligence–based, volumetric assessment of the bone marrow metabolic activity in [ 18 F]FDG PET/CT predicts survival in multiple myeloma
  • 2024
  • Ingår i: European Journal of Nuclear Medicine and Molecular Imaging. - 1619-7070 .- 1619-7089. ; 51:8, s. 2293-2307
  • Tidskriftsartikel (refereegranskat)abstract
    • Purpose: Multiple myeloma (MM) is a highly heterogeneous disease with wide variations in patient outcome. [18F]FDG PET/CT can provide prognostic information in MM, but it is hampered by issues regarding standardization of scan interpretation. Our group has recently demonstrated the feasibility of automated, volumetric assessment of bone marrow (BM) metabolic activity on PET/CT using a novel artificial intelligence (AI)–based tool. Accordingly, the aim of the current study is to investigate the prognostic role of whole-body calculations of BM metabolism in patients with newly diagnosed MM using this AI tool. Materials and methods: Forty-four, previously untreated MM patients underwent whole-body [18F]FDG PET/CT. Automated PET/CT image segmentation and volumetric quantification of BM metabolism were based on an initial CT-based segmentation of the skeleton, its transfer to the standardized uptake value (SUV) PET images, subsequent application of different SUV thresholds, and refinement of the resulting regions using postprocessing. In the present analysis, ten different uptake thresholds (AI approaches), based on reference organs or absolute SUV values, were applied for definition of pathological tracer uptake and subsequent calculation of the whole-body metabolic tumor volume (MTV) and total lesion glycolysis (TLG). Correlation analysis was performed between the automated PET values and histopathological results of the BM as well as patients’ progression-free survival (PFS) and overall survival (OS). Receiver operating characteristic (ROC) curve analysis was used to investigate the discrimination performance of MTV and TLG for prediction of 2-year PFS. The prognostic performance of the new Italian Myeloma criteria for PET Use (IMPeTUs) was also investigated. Results: Median follow-up [95% CI] of the patient cohort was 110 months [105–123 months]. AI-based BM segmentation and calculation of MTV and TLG were feasible in all patients. A significant, positive, moderate correlation was observed between the automated quantitative whole-body PET/CT parameters, MTV and TLG, and BM plasma cell infiltration for all ten [18F]FDG uptake thresholds. With regard to PFS, univariable analysis for both MTV and TLG predicted patient outcome reasonably well for all AI approaches. Adjusting for cytogenetic abnormalities and BM plasma cell infiltration rate, multivariable analysis also showed prognostic significance for high MTV, which defined pathological [18F]FDG uptake in the BM via the liver. In terms of OS, univariable and multivariable analysis showed that whole-body MTV, again mainly using liver uptake as reference, was significantly associated with shorter survival. In line with these findings, ROC curve analysis showed that MTV and TLG, assessed using liver-based cut-offs, could predict 2-year PFS rates. The application of IMPeTUs showed that the number of focal hypermetabolic BM lesions and extramedullary disease had an adverse effect on PFS. Conclusions: The AI-based, whole-body calculations of BM metabolism via the parameters MTV and TLG not only correlate with the degree of BM plasma cell infiltration, but also predict patient survival in MM. In particular, the parameter MTV, using the liver uptake as reference for BM segmentation, provides solid prognostic information for disease progression. In addition to highlighting the prognostic significance of automated, global volumetric estimation of metabolic tumor burden, these data open up new perspectives towards solving the complex problem of interpreting PET scans in MM with a simple, fast, and robust method that is not affected by operator-dependent interventions.
  •  
29.
  • Sadik, May, 1970, et al. (författare)
  • Analytical validation of an automated method for segmentation of the prostate gland in CT images
  • 2017
  • Ingår i: European Journal of Nuclear Medicine and Molecular Imaging. - : Springer Science and Business Media LLC. - 1619-7070 .- 1619-7089. ; 44:supplement issue 2
  • Tidskriftsartikel (refereegranskat)abstract
    • Aim : Uptake of PET tracers in the prostate gland may serve as guidance for management of patients with prostate cancer. PET studies alone do, however, not allow for accurate segmentation of the gland, instead the corresponding CT images contain the required anatomical information. Our long-term aim is to develop an objectively measured PET/CT imaging biomarker reflecting PET tracer uptake. In this study we take the first step and develop and validate a completely automated method for 3D-segmentation of the prostate gland in CT images. Methods : A convolutional neural network (CNN) was trained to segment the prostate gland in CT images using manual segmentations performed by a radiologist in a group of 100 patients, who had undergone 18F-FDG PET/CT. After the training process, the CNN automatically segmented the prostate gland in CT images in a separate validation group consisting of 45 patients with prostate cancer. All patients had undergone a 18F-choline PET/CT as part of a previous research project. The CNN segmentations were compared to manual segmentations performed independently by two radiologists. The volume of the prostate gland was calculated based on segmentations by the CNN and radiologists. The Sørensen-Dice index was used to analyse the overlap between the segmentations by the CNN and the two radiologists. Results : The prostate volumes were on average 79mL (range 9-212mL) in the 45 patients, measured as mean volumes for the two radiologists. The mean difference in prostate volumes between the two radiologists was 14mL (SD 29mL). The mean volume difference between the CNN segmentation and the mean values from the two radiologists was 22mL (SD 43mL). For the subgroup of patients with prostate volumes <100 mL (n=36), the difference between the radiologists was 9mL (SD 11mL) compared to difference CNN vs radiologists of 7mL (SD 15mL). The Sørensen-Dice index was 0.69 and 0.70 for the comparison between CNN segmentation and the two radiologists, respectively and 0.83 for the comparison between the two radiologists. The corresponding Sørensen-Dice index in the 36 patients with volumes <100 mL were 0.74, 0.75 and 0.83, respectively  Conclusion : Our CNN based method for automated segmentation of the prostate gland in CT images show good agreement with the corresponding manual segmentations by two radiologists especially for prostade glands with a volume less than 100 mL.
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30.
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31.
  • Sadik, May, 1970, et al. (författare)
  • Artificial intelligence could alert for focal skeleton/bone marrow uptake in Hodgkin’s lymphoma patients staged with FDG-PET/CT
  • 2021
  • Ingår i: Scientific Reports. - : Springer Science and Business Media LLC. - 2045-2322 .- 2045-2322. ; 11:1
  • Tidskriftsartikel (refereegranskat)abstract
    • To develop an artificial intelligence (AI)-based method for the detection of focal skeleton/bone marrow uptake (BMU) in patients with Hodgkin’s lymphoma (HL) undergoing staging with FDG-PET/CT. The results of the AI in a separate test group were compared to the interpretations of independent physicians. The skeleton and bone marrow were segmented using a convolutional neural network. The training of AI was based on 153 un-treated patients. Bone uptake significantly higher than the mean BMU was marked as abnormal, and an index, based on the total squared abnormal uptake, was computed to identify the focal uptake. Patients with an index above a predefined threshold were interpreted as having focal uptake. As the test group, 48 un-treated patients who had undergone a staging FDG-PET/CT between 2017–2018 with biopsy-proven HL were retrospectively included. Ten physicians classified the 48 cases regarding focal skeleton/BMU. The majority of the physicians agreed with the AI in 39/48 cases (81%) regarding focal skeleton/bone marrow involvement. Inter-observer agreement between the physicians was moderate, Kappa 0.51 (range 0.25–0.80). An AI-based method can be developed to highlight suspicious focal skeleton/BMU in HL patients staged with FDG-PET/CT. Inter-observer agreement regarding focal BMU is moderate among nuclear medicine physicians.
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32.
  • Sadik, May, 1970, et al. (författare)
  • Automated evaluation of normal uptake in different skeletal parts using 18F-sodium fluoride (NaF) PET/CT using a new convolutional neural network method
  • 2017
  • Ingår i: European Journal of Nuclear Medicine and Molecular Imaging. - : Springer Science and Business Media LLC. - 1619-7070 .- 1619-7089. ; 44:Supplement 2, s. 479-479
  • Tidskriftsartikel (refereegranskat)abstract
    • Introduction : Understanding normal skeletal uptake of 18F-sodium fluoride (18F-NaF) in positron emission tomography/computed tomography (PET/CT) is important for clinical interpretation. Quantification of tracer uptake in PET/CT is often performed by placing a volume of interest (VOI) to measure standard uptake values (SUVs). Manual placement of this VOI requires a subjective decision and can only measure uptake in a specific part of the bone. The aim of this study was to investigate normal 18F-NaF skeletal activity in patients with prostate cancer at a stage of the disease prior to development of bone metastases, by using a new method that quantifies uptake in entire skeletal parts. Material and Methods : Patients with biopsy-verified high-risk prostate cancer and a negative or inconclusive bone scintigraphy and no metastatic lesions on 18F-NaF PET/CT (performed March 2008 - June 2010) were retrospectively included (n=48). Whole-body PET scans were acquired 1-1.5 h after i.v. injection of 4 MBq/kg 18F-NaF (max 400 MBq). CT scans were obtained immediately after the PET scan. Thoracic and lumbar vertebrae, sacrum, pelvis, ribs, scapulae, clavicles and sternum were automatically segmented in the CT images, using a method based on a convolutional neural network, to obtain the volume of each skeletal region. The network was trained using a separate group of CT scans with manual segmentations. Mean and maximum SUV (SUVmean and SUVmax) were subsequently measured for each skeletal part in the PET scans. Results : Average (SD) SUVmean for the skeletal regions were: Thoracic vertebrae 0.98 (0.20), lumbar vertebrae 0.96 (0.19), sacrum 0.75 (0.15), pelvis 0.73 (0.16), ribs 0.41 (0.11), scapulae 0.46 (0.11), clavicles 0.50 (0.16) and sternum 0.61 (0.13). Average (SD) SUVmax for the skeletal regions were: Thoracic vertebrae 1.95 (0.66), lumbar vertebrae 2.10 (0.78), sacrum 2.22 (0.77), pelvis 1.99 (0.82), ribs 1.19 (0.35), scapulae 1.94 (0.98), clavicles 2.00 (1.03) and sternum 1.68 (0.44). Conclusion : We present a new method to segment and quantify uptake in skeletal regions in 18F-NaF PET/CT. Various parts of the bone have different SUVs in patients with regional prostate cancer. Vertebrae and pelvis have higher SUVs than ribs. The highest SUVmax were found in the thoracic and lumbar vertebrae. The findings are of importance for interpretation of 18F-NaF PET/CT.
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33.
  • Sadik, May, 1970, et al. (författare)
  • Automated quantification of reference levels in liver and mediastinal blood pool for the Deauville therapy response classification using FDG-PET/CT in Hodgkin and non-Hodgkin lymphomas
  • 2019
  • Ingår i: Clinical Physiology and Functional Imaging. - : Wiley. - 1475-0961 .- 1475-097X. ; 39:1, s. 78-84
  • Tidskriftsartikel (refereegranskat)abstract
    • Background 18F-FDG-PET/CT has become a standard for assessing treatment response in patients with lymphoma. A subjective interpretation of the scan based on the Deauville 5-point scale has been widely adopted. However, inter-observer variability due to the subjectivity of the interpretation is a limitation. Our main goal is to develop an objective and automated method for evaluating response. The first step is to develop and validate an artificial intelligence (AI)-based method, for the automated quantification of reference levels in the liver and mediastinal blood pool in patients with lymphoma. Methods Results The AI-based method was trained to segment the liver and the mediastinal blood pool in CT images from 80 lymphoma patients, who had undergone 18F-FDG-PET/CT, and apply this to a validation group of six lymphoma patients. CT segmentations were transferred to the PET images to obtain automatic standardized uptake values (SUV). The AI-based analysis was compared to corresponding manual segmentations performed by two radiologists. The mean difference for the comparison between the AI-based liver SUV quantifications and those of the two radiologists in the validation group was 0 center dot 02 and 0 center dot 02, respectively, and 0 center dot 02 and 0 center dot 02 for mediastinal blood pool respectively. Conclusions An AI-based method for the automated quantification of reference levels in the liver and mediastinal blood pool shows good agreement with results obtained by experienced radiologists who had manually segmented the CT images. This is a first, promising step towards objective treatment response evaluation in patients with lymphoma based on 18F-FDG-PET/CT.
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34.
  • Sadik, May, 1970, et al. (författare)
  • Convolutional neural networks for segmentation of 49 selected bones in CT images show high reproducibility
  • 2017
  • Ingår i: European Journal of Nuclear Medicine and Molecular Imaging. - 1619-7070 .- 1619-7089. ; 44:Supplement 2
  • Tidskriftsartikel (refereegranskat)abstract
    • Aim : An automated method to calculate Bone Scan Index (BSI) from bone scans has recently been established as a first imaging biomarker in patients with metastatic prostate cancer. BSI has shown to be an independent predictor of survival. PET/CT is more accurate than bone scans in detecting bone metastases. We therefore decided to develop an automated PET/CT based imaging biomarker for assessment of tumor burden in bone. The aim of this project was to develop a method for automated segmentation and volume calculation of bones in CT images, which is the first step in the process of developing a PET/CT based imaging biomarker. Materials and Methods : Convolutional neural networks (CNN) were trained to segment 49 selected bones (12 thoracic vertebrae, 5 lumbar vertebrae, sacrum, 2 hip bones, 24 ribs, 2 scapulae, 2 clavicles and the sternum) using manual segmentations in CT images from 23 patients performed by experienced image readers. Anatomical landmarks were detected using a CNN and pruned using a shape model. These landmarks and the CT image were fed to a second CNN, segmenting the 49 selected bones. After the training process, the CNN segmented the bones in CT images in a separate validation group consisting of 46 patients with prostate cancer. All patients had undergone both 18F-Choline and 18F-NaF PET/CT within a time frame of 3 weeks as part of a previous research project. The two CT scans from each patient were segmented by the CNN and the two volumes of each bone were calculated. Results : The total volume of the 49 bones was on average 3,086 mL in the 46 patients. The individual bones ranged in volume from 8 mL (left 12th rib) to 440 mL (left hip bone). The reproducibility measured as ratio volume difference/mean volume was on average less than 2% for all bones except for the ribs. The mean volumes, differences and reproducibility for the bones of five anatomical regions were as follow: thoracic vertebrae 39mL, 0.6mL, 1.5%; lumbar vertebra 71mL, 0.8 mL, 1.2%; sacrum, hip bones 386mL, 0.9mL, 0.3%; ribs 26mL, 2.0mL, 8.5%; scapulae, clavicles, sternum 97mL, -0.1mL, -0.4%. Conclusion : Our CNN based method for automated segmentation of bones in CT images showed high reproducibility. A reproducible way to segment the skeleton and to measure the bone volume will be important in the development of a PET index relating volumes of abnormal PET tracer uptake to the bone volume.
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35.
  • Sadik, May, 1970, et al. (författare)
  • Variability in reference levels for Deauville classifications applied to lymphoma patients examined with 18F-FDG-PET/CT
  • 2017
  • Ingår i: European Journal of Nuclear Medicine and Molecular Imaging. - 1619-7070 .- 1619-7089. ; 44
  • Tidskriftsartikel (refereegranskat)abstract
    • Aim : To develop and validate a convolutional neural network (CNN) based method for automated quantification of reference levels in liver and mediastinum (blood pool) for the Deauville therapy response classification using FDG-PET/CT in lymphoma patients. Methods : CNNs were trained to segment the liver and the mediastinum, defined as the thoracic part of the aorta, in CT images from 81 consecutive lymphoma patients, who had undergone FDG-PET/CT examinations. Trained image readers segmented the liver and aorta manually in each of the CT images and these segmentations together with the CT images were used to train the CNN. After the training process, the CNN method was applied to a separate validation group consisting of six consecutive lymphoma patients (17-82 years, 3 female). First, the liver and mediastinum were automatically segmented in the CT images. Second, voxels in the corresponding FDG-PET images, which were localized in the liver and mediastinum, were selected and the median standard uptake value (SUV) was calculated. The CNN based analysis was compared to corresponding manual segmentations by two experienced radiologists. The Dice index was used to analyse the overlap between the segmentations by the CNN and the two radiologists. A Dice index of 1.00 indicates perfect matching. Results : The mean Dice indices for the comparison between CNN based liver segmentations and those of the two radiologists in the validation group were 0.95 and 0.95. A corresponding comparison between the two radiologists also resulted in a Dice index of 0.95. The mean liver volumes were 1,752ml, 1,757ml and 1,768ml for the CNN and two radiologists, respectively. The median SUV for the liver was on average 1.8 and the differences between median SUV based on CNN and manual segmentations were less or equal to 0.1. The mean Dice indices for the mediastinum were 0.80, 0.83 (CNN vs radiologists) and 0.86 (comparing the two radiologists). The mean mediastinum (aorta) volumes were 147ml, 140ml and 125ml for the CNN and two radiologists, respectively. The median SUV for the mediastinum was on average 1.4 and the differences between median SUV based on CNN and manual segmentations were less or equal to 0.14. Conclusion : A CNN based method for automated quantification of reference levels in liver and mediastinum show good agreement with results obtained by experienced radiologists, who manually segmented the CT images. This is a first and promising step towards a completely objective treatment response evaluation in patients with lymphoma based on FDG-PET/CT.
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36.
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37.
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38.
  • Saito, Shintaro, et al. (författare)
  • Convolutional neural network-based automatic heart segmentation and quantitation in 123 I-metaiodobenzylguanidine SPECT imaging
  • 2021
  • Ingår i: EJNMMI Research. - : Springer Science and Business Media LLC. - 2191-219X. ; 11:1
  • Tidskriftsartikel (refereegranskat)abstract
    • Background: Since three-dimensional segmentation of cardiac region in 123I-metaiodobenzylguanidine (MIBG) study has not been established, this study aimed to achieve organ segmentation using a convolutional neural network (CNN) with 123I-MIBG single photon emission computed tomography (SPECT) imaging, to calculate heart counts and washout rates (WR) automatically and to compare with conventional quantitation based on planar imaging. Methods: We assessed 48 patients (aged 68.4 ± 11.7 years) with heart and neurological diseases, including chronic heart failure, dementia with Lewy bodies, and Parkinson's disease. All patients were assessed by early and late 123I-MIBG planar and SPECT imaging. The CNN was initially trained to individually segment the lungs and liver on early and late SPECT images. The segmentation masks were aligned, and then, the CNN was trained to directly segment the heart, and all models were evaluated using fourfold cross-validation. The CNN-based average heart counts and WR were calculated and compared with those determined using planar parameters. The CNN-based SPECT and conventional planar heart counts were corrected by physical time decay, injected dose of 123I-MIBG, and body weight. We also divided WR into normal and abnormal groups from linear regression lines determined by the relationship between planar WR and CNN-based WR and then analyzed agreement between them. Results: The CNN segmented the cardiac region in patients with normal and reduced uptake. The CNN-based SPECT heart counts significantly correlated with conventional planar heart counts with and without background correction and a planar heart-to-mediastinum ratio (R2 = 0.862, 0.827, and 0.729, p < 0.0001, respectively). The CNN-based and planar WRs also correlated with and without background correction and WR based on heart-to-mediastinum ratios of R2 = 0.584, 0.568 and 0.507, respectively (p < 0.0001). Contingency table findings of high and low WR (cutoffs: 34% and 30% for planar and SPECT studies, respectively) showed 87.2% agreement between CNN-based and planar methods. Conclusions: The CNN could create segmentation from SPECT images, and average heart counts and WR were reliably calculated three-dimensionally, which might be a novel approach to quantifying SPECT images of innervation.
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39.
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40.
  • Sartor, Hanna, et al. (författare)
  • Auto-segmentations by convolutional neural network in cervical and anorectal cancer with clinical structure sets as the ground truth
  • 2020
  • Ingår i: Clinical and Translational Radiation Oncology. - : Elsevier BV. - 2405-6308. ; 25, s. 37-45
  • Tidskriftsartikel (refereegranskat)abstract
    • Background: It is time-consuming for oncologists to delineate volumes for radiotherapy treatment in computer tomography (CT) images. Automatic delineation based on image processing exists, but with varied accuracy and moderate time savings. Using convolutional neural network (CNN), delineations of volumes are faster and more accurate. We have used CTs with the annotated structure sets to train and evaluate a CNN. Material and methods: The CNN is a standard segmentation network modified to minimize memory usage. We used CTs and structure sets from 75 cervical cancers and 191 anorectal cancers receiving radiation therapy at Skåne University Hospital 2014-2018. Five structures were investigated: left/right femoral heads, bladder, bowel bag, and clinical target volume of lymph nodes (CTVNs). Dice score and mean surface distance (MSD) (mm) evaluated accuracy, and one oncologist qualitatively evaluated auto-segmentations. Results: Median Dice/MSD scores for anorectal cancer: 0.91–0.92/1.93–1.86 femoral heads, 0.94/2.07 bladder, and 0.83/6.80 bowel bag. Median Dice scores for cervical cancer were 0.93–0.94/1.42–1.49 femoral heads, 0.84/3.51 bladder, 0.88/5.80 bowel bag, and 0.82/3.89 CTVNs. With qualitative evaluation, performance on femoral heads and bladder auto-segmentations was mostly excellent, but CTVN auto-segmentations were not acceptable to a larger extent. Discussion: It is possible to train a CNN with high overlap using structure sets as ground truth. Manually delineated pelvic volumes from structure sets do not always strictly follow volume boundaries and are sometimes inaccurately defined, which leads to similar inaccuracies in the CNN output. More data that is consistently annotated is needed to achieve higher CNN accuracy and to enable future clinical implementation.
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41.
  • Simayijiang, Zhayida, et al. (författare)
  • Exploratory study of EEG burst characteristics in preterm infants
  • 2013
  • Ingår i: Engineering in Medicine and Biology Society (EMBC), 2013 35th Annual International Conference of the IEEE. - 1557-170X. ; , s. 4295-4298
  • Konferensbidrag (refereegranskat)abstract
    • In this paper, we study machine learning techniques and features of electroencephalography activity bursts for predicting outcome in extremely preterm infants. It was previously shown that the distribution of interburst interval durations predicts clinical outcome, but in previous work the information within the bursts has been neglected. In this paper, we perform exploratory analysis of feature extraction of burst characteristics and use machine learning techniques to show that such features could be used for outcome prediction. The results are promising, but further verification of the results in larger datasets is needed to obtain conclusive results.
  •  
42.
  • Strandmark, Petter, et al. (författare)
  • HEp-2 Staining Pattern Classification
  • 2012
  • Ingår i: Pattern Recognition (ICPR), 2012 21st International Conference on. - 9781467322164
  • Konferensbidrag (refereegranskat)abstract
    • Classifying images of HEp-2 cells from indirect immunofluorescence has important clinical applications. We have developed an automatic method based on random forests that classifies an HEp-2 cell image into one of six classes. The method is applied to the data set of the ICPR 2012 contest. The previously obtained best accuracy is 79.3% for this data set, whereas we obtain an accuracy of 97.4%. The key to our result is due to carefully designed feature descriptors for multiple level sets of the image intensity. These features characterize both the appearance and the shape of the cell image in a robust manner.
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43.
  • Strandmark, Petter, et al. (författare)
  • Shortest Paths with Curvature and Torsion
  • 2013
  • Ingår i: Computer Vision (ICCV), 2013 IEEE International Conference on. - 1550-5499. - 9781479928392 ; , s. 2024-2031
  • Konferensbidrag (refereegranskat)abstract
    • This paper describes a method of finding thin, elongated structures in images and volumes. We use shortest paths to minimize very general functionals of higher-order curve properties, such as curvature and torsion. Our globally optimal method uses line graphs and its runtime is polynomial in the size of the discretization, often in the order of seconds on a single computer. To our knowledge, we are the first to perform experiments in three dimensions with curvature and torsion regularization. The largest graphs we process have almost one hundred billion arcs. Experiments on medical images and in multi-view reconstruction show the significance and practical usefulness of regularization based on curvature while torsion is still only tractable for small-scale problems
  •  
44.
  • Trägårdh, Elin, et al. (författare)
  • Freely Available, Fully Automated AI-Based Analysis of Primary Tumour and Metastases of Prostate Cancer in Whole-Body [F-18]-PSMA-1007 PET-CT
  • 2022
  • Ingår i: Diagnostics. - : MDPI AG. - 2075-4418. ; 12:9
  • Tidskriftsartikel (refereegranskat)abstract
    • Here, we aimed to develop and validate a fully automated artificial intelligence (AI)-based method for the detection and quantification of suspected prostate tumour/local recurrence, lymph node metastases, and bone metastases from [F-18]PSMA-1007 positron emission tomography-computed tomography (PET-CT) images. Images from 660 patients were included. Segmentations by one expert reader were ground truth. A convolutional neural network (CNN) was developed and trained on a training set, and the performance was tested on a separate test set of 120 patients. The AI method was compared with manual segmentations performed by several nuclear medicine physicians. Assessment of tumour burden (total lesion volume (TLV) and total lesion uptake (TLU)) was performed. The sensitivity of the AI method was, on average, 79% for detecting prostate tumour/recurrence, 79% for lymph node metastases, and 62% for bone metastases. On average, nuclear medicine physicians' corresponding sensitivities were 78%, 78%, and 59%, respectively. The correlations of TLV and TLU between AI and nuclear medicine physicians were all statistically significant and ranged from R = 0.53 to R = 0.83. In conclusion, the development of an AI-based method for prostate cancer detection with sensitivity on par with nuclear medicine physicians was possible. The developed AI tool is freely available for researchers.
  •  
45.
  • Trägårdh, Elin, et al. (författare)
  • Improving sensitivity through data augmentation with synthetic lymph node metastases for AI-based analysis of PSMA PET-CT images
  • 2024
  • Ingår i: Clinical Physiology and Functional Imaging. - 1475-0961 .- 1475-097X. ; 44:4, s. 332-339
  • Tidskriftsartikel (refereegranskat)abstract
    • Background: We developed a fully automated artificial intelligence (AI)AI-based-based method for detecting suspected lymph node metastases in prostate-specific membrane antigen (PSMA)(PSMA) positron emission tomography-computed tomography (PET-CT)(PET-CT) images of prostate cancer patients by using data augmentation that adds synthetic lymph node metastases to the images to expand the training set. Methods: Synthetic data were derived from original training images to which new synthetic lymph node metastases were added. Thus, the original training set from a previous study (n = 420) was expanded by one synthetic image for every original image (n = 840), which was used to train an AI model. The performance of the AI model was compared to that of nuclear medicine physicians and a previously developed AI model. The human readers were alternately used as a reference and compared to either another reading or AI model. Results: The new AI model had an average sensitivity of 84% for detecting lymph node metastases compared with 78% for human readings. Our previously developed AI method without synthetic data had an average sensitivity of 79%. The number of false positive lesions were slightly higher for the new AI model (average 3.3 instances per patient) compared to human readings and the previous AI model (average 2.8 instances per patient), while the number of false negative lesions was lower. Conclusions: Creating synthetic lymph node metastases, as a form of data augmentation, on [18F]PSMA-1007F]PSMA-1007 PETPET-CT-CT images improved the sensitivity of an AI model for detecting suspected lymph node metastases. However, the number of false positive lesions increased somewhat.
  •  
46.
  • Trägårdh, Elin, et al. (författare)
  • RECOMIA-a cloud-based platform for artificial intelligence research in nuclear medicine and radiology
  • 2020
  • Ingår i: Ejnmmi Physics. - : Springer Science and Business Media LLC. - 2197-7364. ; 7:1
  • Tidskriftsartikel (refereegranskat)abstract
    • Background: Artificial intelligence (AI) is about to transform medical imaging. The Research Consortium for Medical Image Analysis (RECOMIA), a not-for-profit organisation, has developed an online platform to facilitate collaboration between medical researchers and AI researchers. The aim is to minimise the time and effort researchers need to spend on technical aspects, such as transfer, display, and annotation of images, as well as legal aspects, such as de-identification. The purpose of this article is to present the RECOMIA platform and its AI-based tools for organ segmentation in computed tomography (CT), which can be used for extraction of standardised uptake values from the corresponding positron emission tomography (PET) image. Results: The RECOMIA platform includes modules for (1) local de-identification of medical images, (2) secure transfer of images to the cloud-based platform, (3) display functions available using a standard web browser, (4) tools for manual annotation of organs or pathology in the images, (5) deep learning-based tools for organ segmentation or other customised analyses, (6) tools for quantification of segmented volumes, and (7) an export function for the quantitative results. The AI-based tool for organ segmentation in CT currently handles 100 organs (77 bones and 23 soft tissue organs). The segmentation is based on two convolutional neural networks (CNNs): one network to handle organs with multiple similar instances, such as vertebrae and ribs, and one network for all other organs. The CNNs have been trained using CT studies from 339 patients. Experienced radiologists annotated organs in the CT studies. The performance of the segmentation tool, measured as mean Dice index on a manually annotated test set, with 10 representative organs, was 0.93 for all foreground voxels, and the mean Dice index over the organs were 0.86 (0.82 for the soft tissue organs and 0.90 for the bones). Conclusion: The paper presents a platform that provides deep learning-based tools that can perform basic organ segmentations in CT, which can then be used to automatically obtain the different measurement in the corresponding PET image. The RECOMIA platform is available on request atfor research purposes.
  •  
47.
  • Ulen, Barbro, et al. (författare)
  • Assessing strategies to mitigate phosphorus leaching from drained clay soils
  • 2018
  • Ingår i: AMBIO: A Journal of the Human Environment. - : Springer Science and Business Media LLC. - 0044-7447 .- 1654-7209. ; 47, s. 114-123
  • Tidskriftsartikel (refereegranskat)abstract
    • Assessing mitigation of phosphorus (P) leaching from subsurface drainage systems is challenging due to high spatial and temporal variation in leaching. Mean measured total P leaching from a clayey soil in an eight-year study period (four replicates per treatment) was (kg ha(-1) year(-1)): 1.21 from shallow autumn tillage (ShT), 0.84 from unfertilised fallow (UF), 0.81 from conventional autumn ploughing (CT) and 0.57 from structure liming (SL-CT). Treatment was not significant using Richards-Baker flow index or a distance factor as covariate (p = 0.084 and 0.057). A tendency for lower leaching was obtained comparing SL-CT with ShT (p (adjusted) = 0.060 and 0.009 respectively). A combination of measures adapted to drainage conditions and clay content in different parts of the field is proposed since P leaching was approximately halved from an adjacent field (4.3 ha) in a three-year post-period compared with a three-year pre-period for structure liming the entire field and drainage system renovation plus structure lime drain backfilling.
  •  
48.
  •  
49.
  • Ulén, Johannes, et al. (författare)
  • An Efficient Optimization Framework for Multi-Region Segmentation based on Lagrangian Duality
  • 2013
  • Ingår i: IEEE Transactions on Medical Imaging. - 1558-254X. ; 32:2, s. 178-188
  • Tidskriftsartikel (refereegranskat)abstract
    • We introduce a multi-region model for simultaneous segmentation of medical images. In contrast to many other models, geometric constraints such as inclusion and exclusion between the regions are enforced, which makes it possible to correctly segment different regions even if the intensity distributions are identical. We efficiently optimize the model using a combination of graph cuts and Lagrangian duality which is faster and more memory efficient than current state of the art. As the method is based on global optimization techniques, the resulting segmentations are independent of initialization. We apply our framework to the segmentation of the left and right ventricles, myocardium and the left ventricular papillary muscles in MRI and to lung segmentation in full-body X-ray CT. We evaluate our approach on a publicly available benchmark with competitive results.
  •  
50.
  • Ulén, Johannes (författare)
  • Higher-Order Regularization in Computer Vision
  • 2014
  • Doktorsavhandling (övrigt vetenskapligt/konstnärligt)abstract
    • At the core of many computer vision models lies the minimization of an objective function consisting of a sum of functions with few arguments. The order of the objective function is defined as the highest number of arguments of any summand. To reduce ambiguity and noise in the solution, regularization terms are included into the objective function, enforcing different properties of the solution. The most commonly used regularization is penalization of boundary length, which requires a second-order objective function. Most of this thesis is devoted to introducing higher-order regularization terms and presenting efficient minimization schemes. One of the topics of the thesis covers a reformulation of a large class of discrete functions into an equivalent form. The reformulation is shown, both in theory and practical experiments, to be advantageous for higher-order regularization models based on curvature and second-order derivatives. Another topic is the parametric max-flow problem. An analysis is given, showing its inherent limitations for large-scale problems which are common in computer vision. The thesis also introduces a segmentation approach for finding thin and elongated structures in 3D volumes. Using a line-graph formulation, it is shown how to efficiently regularize with respect to higher-order differential geometric properties such as curvature and torsion. Furthermore, an efficient optimization approach for a multi-region model is presented which, in addition to standard regularization, is able to enforce geometric constraints such as inclusion or exclusion of different regions. The final part of the thesis deals with dense stereo estimation. A new regularization model is introduced, penalizing the second-order derivatives of a depth or disparity map. Compared to previous second-order approaches to dense stereo estimation, the new regularization model is shown to be more easily optimized.
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