| 1. |
- Astaraki, Mehdi, PhD Student, 1984-, et al.
(författare)
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A Comparative Study of Radiomics and Deep-Learning Based Methods for Pulmonary Nodule Malignancy Prediction in Low Dose CT Images
- 2021
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Ingår i: Frontiers in Oncology. - : Frontiers Media SA. - 2234-943X. ; 11
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Tidskriftsartikel (refereegranskat)abstract
- Objectives: Both radiomics and deep learning methods have shown great promise in predicting lesion malignancy in various image-based oncology studies. However, it is still unclear which method to choose for a specific clinical problem given the access to the same amount of training data. In this study, we try to compare the performance of a series of carefully selected conventional radiomics methods, end-to-end deep learning models, and deep-feature based radiomics pipelines for pulmonary nodule malignancy prediction on an open database that consists of 1297 manually delineated lung nodules.Methods: Conventional radiomics analysis was conducted by extracting standard handcrafted features from target nodule images. Several end-to-end deep classifier networks, including VGG, ResNet, DenseNet, and EfficientNet were employed to identify lung nodule malignancy as well. In addition to the baseline implementations, we also investigated the importance of feature selection and class balancing, as well as separating the features learned in the nodule target region and the background/context region. By pooling the radiomics and deep features together in a hybrid feature set, we investigated the compatibility of these two sets with respect to malignancy prediction.Results: The best baseline conventional radiomics model, deep learning model, and deep-feature based radiomics model achieved AUROC values (mean ± standard deviations) of 0.792 ± 0.025, 0.801 ± 0.018, and 0.817 ± 0.032, respectively through 5-fold cross-validation analyses. However, after trying out several optimization techniques, such as feature selection and data balancing, as well as adding context features, the corresponding best radiomics, end-to-end deep learning, and deep-feature based models achieved AUROC values of 0.921 ± 0.010, 0.824 ± 0.021, and 0.936 ± 0.011, respectively. We achieved the best prediction accuracy from the hybrid feature set (AUROC: 0.938 ± 0.010).Conclusion: The end-to-end deep-learning model outperforms conventional radiomics out of the box without much fine-tuning. On the other hand, fine-tuning the models lead to significant improvements in the prediction performance where the conventional and deep-feature based radiomics models achieved comparable results. The hybrid radiomics method seems to be the most promising model for lung nodule malignancy prediction in this comparative study.
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| 2. |
- Astaraki, Mehdi, PhD Student, 1984-, et al.
(författare)
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Benign-malignant pulmonary nodule classification in low-dose CT with convolutional features
- 2021
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Ingår i: Physica medica (Testo stampato). - : Elsevier BV. - 1120-1797 .- 1724-191X. ; 83, s. 146-153
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Tidskriftsartikel (refereegranskat)abstract
- Purpose: Low-Dose Computed Tomography (LDCT) is the most common imaging modality for lung cancer diagnosis. The presence of nodules in the scans does not necessarily portend lung cancer, as there is an intricate relationship between nodule characteristics and lung cancer. Therefore, benign-malignant pulmonary nodule classification at early detection is a crucial step to improve diagnosis and prolong patient survival. The aim of this study is to propose a method for predicting nodule malignancy based on deep abstract features.Methods: To efficiently capture both intra-nodule heterogeneities and contextual information of the pulmonary nodules, a dual pathway model was developed to integrate the intra-nodule characteristics with contextual attributes. The proposed approach was implemented with both supervised and unsupervised learning schemes. A random forest model was added as a second component on top of the networks to generate the classification results. The discrimination power of the model was evaluated by calculating the Area Under the Receiver Operating Characteristic Curve (AUROC) metric. Results: Experiments on 1297 manually segmented nodules show that the integration of context and target supervised deep features have a great potential for accurate prediction, resulting in a discrimination power of 0.936 in terms of AUROC, which outperformed the classification performance of the Kaggle 2017 challenge winner.Conclusion: Empirical results demonstrate that integrating nodule target and context images into a unified network improves the discrimination power, outperforming the conventional single pathway convolutional neural networks.
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| 3. |
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| 4. |
- Blystad, Ida, 1972-, et al.
(författare)
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Quantitative MRI using relaxometry in malignant gliomas detects contrast enhancement in peritumoral oedema
- 2020
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Ingår i: Scientific Reports. - : Springer Nature. - 2045-2322. ; 10:1
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Tidskriftsartikel (refereegranskat)abstract
- Malignant gliomas are primary brain tumours with an infiltrative growth pattern, often with contrast enhancement on magnetic resonance imaging (MRI). However, it is well known that tumour infiltration extends beyond the visible contrast enhancement. The aim of this study was to investigate if there is contrast enhancement not detected visually in the peritumoral oedema of malignant gliomas by using relaxometry with synthetic MRI. 25 patients who had brain tumours with a radiological appearance of malignant glioma were prospectively included. A quantitative MR-sequence measuring longitudinal relaxation (R-1), transverse relaxation (R-2) and proton density (PD), was added to the standard MRI protocol before surgery. Five patients were excluded, and in 20 patients, synthetic MR images were created from the quantitative scans. Manual regions of interest (ROIs) outlined the visibly contrast-enhancing border of the tumours and the peritumoral area. Contrast enhancement was quantified by subtraction of native images from post GD-images, creating an R-1-difference-map. The quantitative R-1-difference-maps showed significant contrast enhancement in the peritumoral area (0.047) compared to normal appearing white matter (0.032), p = 0.048. Relaxometry detects contrast enhancement in the peritumoral area of malignant gliomas. This could represent infiltrative tumour growth.
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| 5. |
- Brusini, Irene, et al.
(författare)
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Shape Information Improves the Cross-Cohort Performance of Deep Learning-Based Segmentation of the Hippocampus
- 2020
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Ingår i: Frontiers in Neuroscience. - : Frontiers Media S.A.. - 1662-4548 .- 1662-453X. ; 14
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Tidskriftsartikel (refereegranskat)abstract
- Performing an accurate segmentation of the hippocampus from brain magnetic resonance images is a crucial task in neuroimaging research, since its structural integrity is strongly related to several neurodegenerative disorders, including Alzheimer's disease (AD). Some automatic segmentation tools are already being used, but, in recent years, new deep learning (DL)-based methods have been proven to be much more accurate in various medical image segmentation tasks. In this work, we propose a DL-based hippocampus segmentation framework that embeds statistical shape of the hippocampus as context information into the deep neural network (DNN). The inclusion of shape information is achieved with three main steps: (1) a U-Net-based segmentation, (2) a shape model estimation, and (3) a second U-Net-based segmentation which uses both the original input data and the fitted shape model. The trained DL architectures were tested on image data of three diagnostic groups [AD patients, subjects with mild cognitive impairment (MCI) and controls] from two cohorts (ADNI and AddNeuroMed). Both intra-cohort validation and cross-cohort validation were performed and compared with the conventional U-net architecture and some variations with other types of context information (i.e., autocontext and tissue-class context). Our results suggest that adding shape information can improve the segmentation accuracy in cross-cohort validation, i.e., when DNNs are trained on one cohort and applied to another. However, no significant benefit is observed in intra-cohort validation, i.e., training and testing DNNs on images from the same cohort. Moreover, compared to other types of context information, the use of shape context was shown to be the most successful in increasing the accuracy, while keeping the computational time in the order of a few minutes.
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| 6. |
- Fuchs, Alexander, 1985-
(författare)
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Assessment of predicting blood flow and atherosclerosis in the aorta and renal arteries
- 2020
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Doktorsavhandling (övrigt vetenskapligt/konstnärligt)abstract
- Cardiovascular diseases (CVD) are the most common cause of death in large parts of the world. Atherosclerosis (AS) has a major part in most CVDs. AS is a slowly developingdisease which is dependent on multiple factors such as genetics and life style (food, smoking, and physical activities). AS is primarily a disease of the arterial wall and develops preferentially at certain locations (such as arterial branches and in certain vessels like thecoronary arteries). The close relation between AS sites and blood flow has been well established over the years. However, due to multi-factorial causes, there exist no early prognostic tools for identifying individuals that should be treated prophylactically or followed up. The underlying hypothesis of this thesis was to determine if it is possible to use bloodflow simulations of patient-specific cases in order to identify individuals with risk for developing AS. CT scans from patients with renal artery stenosis (RAS) were used to get the affected vessels geometry. Blood flow in original and “reconstructed” arteries were simulated. Commonly used wall shear stress (WSS) related indicators of AS were studied to assess their use as risk indicators for developing AS. Divergent results indicated urgent need to assess the impact ofsimulation related factors on results. Altogether, blood flow in the following vessels was studied: The whole aorta with branches from the aortic arch and the abdominal aorta, abdominal aorta as well as the renal arteries, and separately the thoracic aorta with the three main branching arteries from the aortic arch. The impact of geometrical reconstruction, employed boundary conditions (BCs), effects of flow-rate, heart-rate and models of blood viscosity as function of local hematocrit (red blood cell, RBC, concentration) and shear-rate were studied in some detail. In addition to common WSS-related indicators, we suggested the use of endothelial activation models as a further risk indicator. The simulations data was used to extract not only the WSS-related data but also the impact of flow-rate on the extent of retrograde flow in the aorta and close to its walls. The formation of helical motion and flow instabilities (which at high flow- and heart-rate lead to turbulence) was also considered.Results:A large number of simulations (more than 100) were carried out. These simulations assessed the use of flow-rate specified BCs, pressure based BCs or so called windkessel (WK) outlet BCs that simulate effects of peripheral arterial compliance. The results showed high sensitivity of the flow to BCs. For example, the deceleration phase of the flow-rate is more prone to flow instabilities (as also expressed in terms of multiple inflection points in the streamwise velocity profile) as well as leading to retrograde flow. In contrast, the acceleration phase leads to uni-directional and more stable flow. As WSS unsteadiness was found to be pro-AS, it was important to assess the effect flow-rate deceleration, under physiological and pathological conditions. Peaks of retrograde flow occur at local temporal minima in flow-rate. WK BCs require ad-hoc adjusted parameters and are therefore useful only when fully patient specific (i.e. all information is valid for a particular patient at a particular point of time) data is available. Helical flows which are considered as atheroprotective, are formed naturally, depending primarily on the geometry (due to the bends in the thoracic aorta). Helical flow was also observed in the major aortic branches. The helical motion is weaker during flow deceleration and diastole when it may locally also change direction. Most common existing blood viscosity models are based on hematocrit and shear-rate. These models show strong variation of blood (mixture) viscosity. With strong shear-rate blood viscosity is lowest and is almost constant. The impact of blood viscosity in terms of dissipation is counter balanced by the shear-rate; At low shear-rate the blood has larger viscosity and at high shear-rate it is the opposite. This effect and due to the temporal variations in the local flow conditions the effect of blood rheology on the WSS indicators is weak. Tracking of blood components and clot-models shows that the retrograde motion and the flow near branches may have so strong curvature that centrifugal force can become important. This effect may lead to the transport of a thrombus from the descending aorta back to the branches of the aortic arch and could cause embolic stroke. The latter results confirm clinical observation of the risk of stroke due to transport of emboli from the proximal part of the descending aorta upstream to the vessels branching from the aortic arch and which lead blood to the brain.Conclusions:The main reasons for not being able to propose an early predictive tool for future developmentof AS are four-folded:i. At present, the mechanisms behind AS are not adequately understood to enable to define aset of parameters that are sensitive and specific enough to be predictive of its development.ii. The lack of accurate patient-specific data (BC:s) over the whole physiological “envelop”allows only limited number of flow simulations which may not be adequate for patientspecificpredictive purposes.iii. The shortcomings of current models with respect to material properties of blood andarterial walls (for patient-specific space- and time-variations) are lacking.iv. There is a need for better simulation data processing, i.e. tools that enable deducinggeneral predictive atherosclerotic parameters from a limited number of simulations, throughe.g. extending reduced modeling and/or deep learning.The results do show, however, that blood flow simulations may produce very useful data thatenhances understanding of clinically observed processes such as explaining helical- andretrograde flows and the transport of blood components and emboli in larger arteries.
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| 7. |
- Guha, Indranil, et al.
(författare)
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A comparative study of trabecular bone micro-structural measurements using different CT modalities
- 2020
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Ingår i: Physics in Medicine and Biology. - : IOP Publishing. - 0031-9155 .- 1361-6560. ; 65:23, s. 235029-
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Tidskriftsartikel (refereegranskat)abstract
- Osteoporosis, characterized by reduced bone mineral density and micro-architectural degeneration, significantly enhances fracture-risk. There are several viable methods for trabecular bone micro-imaging, which widely vary in terms of technology, reconstruction principle, spatial resolution, and acquisition time. We have performed an excised cadaveric bone specimen study to evaluate different computed tomography (CT)-imaging modalities for trabecular bone micro-structural analysis. Excised cadaveric bone specimens from the distal radius were scanned using micro-CT and four in vivo CT imaging modalities: high-resolution peripheral quantitative computed tomography (HR-pQCT), dental cone beam CT (CBCT), whole-body multi-row detector CT (MDCT), and extremity CBCT. A new algorithm was developed to optimize soft thresholding parameters for individual in vivo CT modalities for computing quantitative bone volume fraction maps. Finally, agreement of trabecular bone micro-structural measures, derived from different in vivo CT imaging, with reference measures from micro-CT imaging was examined. Observed values of most trabecular measures, including trabecular bone volume, network area, transverse and plate-rod micro-structure, thickness, and spacing, for in vivo CT modalities were higher than their micro-CT-based reference values. In general, HR-pQCT-based trabecular bone measures were closer to their reference values as compared to other in vivo CT modalities. Despite large differences in observed values of measures among modalities, high linear correlation (r ∈ [0.94 0.99]) was found between micro-CT and in vivo CT-derived measures of trabecular bone volume, transverse and plate micro-structural volume, and network area. All HR-pQCT-derived trabecular measures, except the erosion index, showed high correlation (r ∈ [0.91 0.99]). The plate-width measure showed a higher correlation (r ∈ [0.72 0.91]) among in vivo and micro-CT modalities than its counterpart binary plate-rod characterization-based measure erosion index (r ∈ [0.65 0.81]). Although a strong correlation was observed between micro-structural measures from in vivo and micro-CT imaging, large shifts in their values for in vivo modalities warrant proper scanner calibration prior to adopting in multi-site and longitudinal studies.
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| 8. |
- Jörgens, Daniel, 1988-
(författare)
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Development and application of rule- and learning-based approaches within the scope of neuroimaging : Tensor voting, tractography and machine learning
- 2020
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Doktorsavhandling (övrigt vetenskapligt/konstnärligt)abstract
- The opportunity to non-invasively probe the structure and function of different parts of the human body makes medical imaging an indispensable tool in clinical diagnostics and related fields of research. Especially neuroscientists rely on modalities like structural or functional Magnetic Resonance Imaging, Computed Tomography or Positron Emission Tomography to study the human brain in vivo. But also in clinical routine, diagnosis, screening or follow-up of different pathological conditions build upon the use of neuroimaging.Computational solutions are essential for the analysis of medical images. While in the case of conventional photography the recorded signal comprises the actual image, most medical imaging devices require the reconstruction of an image from the acquired data. However, not only the image formation, but also further processing tasks to assist doctors or researchers in the interpretation of the data and eventually in subsequent decision making, rely more and more on automation. Typical tasks range from locating and measuring objects in a single patient, e.g. a particular organ, a tumour or a specific region in the brain, to comparing such measurements over time between groups consisting of large numbers of subjects. Automated solutions for these scenarios are required to model complex relations of data in the presence of acquisition noise and subject variability while assuring a tractable computational demand.Traditionally, the development of computational algorithms for medical imaging problems focused on rule-based strategies. Explicitly defined rules that encode the knowledge of the developer are characteristic for such approaches. Within the last decade, this paradigm began to change and learning-based models dramatically gained in popularity. These rely on fitting a complex model to large amounts of data samples, often annotated, which are representative for a particular problem. Instead of manually designing the sought-after solution, it is ‘learned from the data’. While these models have shown enormous potential, they also pose important questions for method developers. How can I get hold of enough data? How much data is enough? How can I obtain proper annotations?This thesis comprises six studies covering the development and the application of methods along the whole pipeline of medical image analysis. Studies I and II propose different extensions to the method of tensor voting to make it applicable in specific medical imaging problems. Studies III–V address the use of modern machine learning techniques, in particular neural networks, in the field of tractography. Notably, the challenge of obtaining adequately annotated data samples is a topic in Study V. In Study VI, a prospective neuroimaging study of unilateral ear canal atresia in adults is presented, covering the application of methods from data acquisition to group comparison. Overall, the compiled works contributed, in one way or the other, to the non-invasive extraction of knowledge from the human body through automated processing of medical images.
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| 9. |
- Kataria, Bharti, 1955-, et al.
(författare)
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Assessment of image quality in abdominal computed tomography : Effect of model-based iterative reconstruction, multi-planar reconstruction and slice thickness on potential dose reduction
- 2020
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Ingår i: European Journal of Radiology. - : Elsevier Ireland Ltd. - 0720-048X .- 1872-7727. ; 122
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Tidskriftsartikel (refereegranskat)abstract
- Purpose: To determine the effect of tube load, model-based iterative reconstruction (MBIR) strength and slice thickness in abdominal CT using visual comparison of multi-planar reconstruction images. Method: Five image criteria were assessed independently by four radiologists on two data sets at 42- and 98-mAs tube loads for 25 patients examined on a 192-slice dual-source CT scanner. Effect of tube load, MBIR strength, slice thickness and potential dose reduction was estimated with Visual Grading Regression (VGR). Objective image quality was determined by measuring noise (SD), contrast-to-noise (CNR) ratio and noise-power spectra (NPS). Results: Comparing 42- and 98-mAs tube loads, improved image quality was observed as a strong effect of log tube load regardless of MBIR strength (p < 0.001). Comparing strength 5 to 3, better image quality was obtained for two criteria (p < 0.01), but inferior for liver parenchyma and overall image quality. Image quality was significantly better for slice thicknesses of 2mm and 3mm compared to 1mm, with potential dose reductions between 24%-41%. As expected, with decrease in slice thickness and algorithm strength, the noise power and SD (HU-values) increased, while the CNR decreased. Conclusion: Increasing slice thickness from 1 mm to 2 mm or 3 mm allows for a possible dose reduction. MBIR strength 5 shows improved image quality for three out of five criteria for 1 mm slice thickness. Increasing MBIR strength from 3 to 5 has diverse effects on image quality. Our findings do not support a general recommendation to replace strength 3 by strength 5 in clinical abdominal CT protocols. However, strength 5 may be used in task-based protocols.
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| 10. |
- Kataria, Bharti, 1955-, et al.
(författare)
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Image Quality and Potential Dose Reduction Using Advanced Modeled Iterative Reconstruction (Admire) in Abdominal Ct : A Review
- 2021
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Ingår i: Radiation Protection Dosimetry. - : Oxford University Press. - 0144-8420 .- 1742-3406. ; 195:3-4, s. 177-187
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Forskningsöversikt (refereegranskat)abstract
- Traditional filtered back projection (FBP) reconstruction methods have served the computed tomography (CT) community wellfor over 40 years. With the increased use of CT during the last decades, efforts to minimise patient exposure, while maintainingsufficient or improved image quality, have led to the development of model-based iterative reconstruction (MBIR) algorithms fromseveral vendors. The usefulness of the advanced modeled iterative reconstruction (ADMIRE) (Siemens Healthineers) MBIR inabdominal CT is reviewed and its noise suppression and/or dose reduction possibilities explored. Quantitative and qualitativemethods with phantom and human subjects were used. Assessment of the quality of phantom images will not always correlatepositively with those of patient images, particularly at the higher strength of the ADMIRE algorithm. With few exceptions,ADMIRE Strength 3 typically allows for substantial noise reduction compared to FBP and hence to significant (≈30%) patientdose reductions. The size of the dose reductions depends on the diagnostic task.
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