| 31. |
- Jimenez-del-Toro, Oscar, et al.
(författare)
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Cloud-Based Evaluation of Anatomical Structure Segmentation and Landmark Detection Algorithms : VISCERAL Anatomy Benchmarks
- 2016
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Ingår i: IEEE Transactions on Medical Imaging. - : Institute of Electrical and Electronics Engineers (IEEE). - 0278-0062 .- 1558-254X. ; 35:11, s. 2459-2475
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Tidskriftsartikel (refereegranskat)abstract
- Variations in the shape and appearance of anatomical structures in medical images are often relevant radiological signs of disease. Automatic tools can help automate parts of this manual process. A cloud-based evaluation framework is presented in this paper including results of benchmarking current state-of-the-art medical imaging algorithms for anatomical structure segmentation and landmark detection: the VISCERAL Anatomy benchmarks. The algorithms are implemented in virtual machines in the cloud where participants can only access the training data and can be run privately by the benchmark administrators to objectively compare their performance in an unseen common test set. Overall, 120 computed tomography and magnetic resonance patient volumes were manually annotated to create a standard Gold Corpus containing a total of 1295 structures and 1760 landmarks. Ten participants contributed with automatic algorithms for the organ segmentation task, and three for the landmark localization task. Different algorithms obtained the best scores in the four available imaging modalities and for subsets of anatomical structures. The annotation framework, resulting data set, evaluation setup, results and performance analysis from the three VISCERAL Anatomy benchmarks are presented in this article. Both the VISCERAL data set and Silver Corpus generated with the fusion of the participant algorithms on a larger set of non-manually-annotated medical images are available to the research community.
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| 32. |
- 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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| 33. |
- Kirişli, Hortense, et al.
(författare)
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Standardized evaluation framework for evaluating coronary artery stenosis detection, stenosis quantification and lumen segmentation algorithms in computed tomography angiography
- 2013
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Ingår i: Medical Image Analysis. - : Elsevier. - 1361-8415 .- 1361-8423. ; 17:8, s. 859-876
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Tidskriftsartikel (refereegranskat)abstract
- Though conventional coronary angiography (CCA) has been the standard of reference for diagnosing coronary artery disease in the past decades, computed tomography angiography (CIA) has rapidly emerged, and is nowadays widely used in clinical practice. Here, we introduce a standardized evaluation framework to reliably evaluate and compare the performance of the algorithms devised to detect and quantify the coronary artery stenoses, and to segment the coronary artery lumen in CIA data. The objective of this evaluation framework is to demonstrate the feasibility of dedicated algorithms to: (I) (semi-)automatically detect and quantify stenosis on CIA, in comparison with quantitative coronary angiography (QCA) and CIA consensus reading, and (2) (semi-)automatically segment the coronary lumen on CIA, in comparison with expert's manual annotation. A database consisting of 48 multicenter multivendor cardiac CIA datasets with corresponding reference standards are described and made available. The algorithms from 11 research groups were quantitatively evaluated and compared. The results show that (1) some of the current stenosis detection/quantification algorithms may be used for triage or as a second-reader in clinical practice, and that (2) automatic lumen segmentation is possible with a precision similar to that obtained by experts. The framework is open for new submissions through the website, at http://coronary.bigr.nl/stenoses/.
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| 34. |
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| 35. |
- Kisonaite, Konstancija, et al.
(författare)
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Automatic estimation of the cross-sectional area of the waist of the nerve fiber layer at the optic nerve head
- 2023
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Ingår i: Acta Ophthalmologica. - : John Wiley & Sons. - 1755-375X .- 1755-3768.
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Tidskriftsartikel (refereegranskat)abstract
- PurposeGlaucoma leads to pathological loss of axons in the retinal nerve fibre layer at the optic nerve head (ONH). This study aimed to develop a strategy for the estimation of the cross-sectional area of the axons in the ONH. Furthermore, improving the estimation of the thickness of the nerve fibre layer, as compared to a method previously published by us.MethodsIn the 3D-OCT image of the ONH, the central limit of the pigment epithelium and the inner limit of the retina, respectively, were identified with deep learning algorithms. The minimal distance was estimated at equidistant angles around the circumference of the ONH. The cross-sectional area was estimated by the computational algorithm. The computational algorithm was applied on 16 non-glaucomatous subjects.ResultsThe mean cross-sectional area of the waist of the nerve fibre layer in the ONH was 1.97 ± 0.19 mm2. The mean difference in minimal thickness of the waist of the nerve fibre layer between our previous and the current strategies was estimated as CIμ (0.95) 0 ± 1 μm (d.f. = 15).ConclusionsThe developed algorithm demonstrated an undulating cross-sectional area of the nerve fibre layer at the ONH. Compared to studies using radial scans, our algorithm resulted in slightly higher values for cross-sectional area, taking the undulations of the nerve fibre layer at the ONH into account. The new algorithm for estimation of the thickness of the waist of the nerve fibre layer in the ONH yielded estimates of the same order as our previous algorithm.
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| 36. |
- Kisonaite, Konstancija, et al.
(författare)
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Estimation of the cross-sectional surface area of the waist of the nerve fiber layer at the optic nerve head
- 2022
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Ingår i: Progress in Biomedical Optics and Imaging. - : SPIE-Intl Soc Optical Eng.
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Konferensbidrag (refereegranskat)abstract
- Glaucoma is a global disease that leads to blindness due to pathological loss of retinal ganglion cell axons in the optic nerve head (ONH). The presented project aims at improving a computational algorithm for estimating the thickness and surface area of the waist of the nerve fiber layer in the ONH. Our currently developed deep learning AI algorithm meets the need for a morphometric parameter that detects glaucomatous change earlier than current clinical follow-up methods. In 3D OCT image volumes, two different AI algorithms identify the Optic nerve head Pigment epithelium Central Limit (OPCL) and the Inner limit of the Retina Closest Point (IRCP) in a 3D grid. Our computational algorithm includes the undulating surface area of the waist of the ONH, as well as waist thickness. In 16 eyes of 16 non-glaucomatous subjects aged [20;30] years, the mean difference in minimal thickness of the waist of the nerve fiber layer between our previous and the current post-processing strategies was estimated as CIμ(0.95) 0 ±1 μm (D.f. 15). The mean surface area of the waist of the nerve fiber layer in the optic nerve head was 1.97 ± 0.19 mm2. Our computational algorithm results in slightly higher values for surface areas compared to published work, but as expected, this may be due to surface undulations of the waist being considered. Estimates of the thickness of the waist of the ONH yields estimates of the same order as our previous computational algorithm.
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| 37. |
- Kumar, Neeraj, et al.
(författare)
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A Multi-Organ Nucleus Segmentation Challenge
- 2020
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Ingår i: IEEE Transactions on Medical Imaging. - : Institute of Electrical and Electronics Engineers (IEEE). - 0278-0062 .- 1558-254X. ; 39:5, s. 1380-1391
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Tidskriftsartikel (refereegranskat)abstract
- Generalized nucleus segmentation techniques can contribute greatly to reducing the time to develop and validate visual biomarkers for new digital pathology datasets. We summarize the results of MoNuSeg 2018 Challenge whose objective was to develop generalizable nuclei segmentation techniques in digital pathology. The challenge was an official satellite event of the MICCAI 2018 conference in which 32 teams with more than 80 participants from geographically diverse institutes participated. Contestants were given a training set with 30 images from seven organs with annotations of 21,623 individual nuclei. A test dataset with 14 images taken from seven organs, including two organs that did not appear in the training set was released without annotations. Entries were evaluated based on average aggregated Jaccard index (AJI) on the test set to prioritize accurate instance segmentation as opposed to mere semantic segmentation. More than half the teams that completed the challenge outperformed a previous baseline. Among the trends observed that contributed to increased accuracy were the use of color normalization as well as heavy data augmentation. Additionally, fully convolutional networks inspired by variants of U-Net, FCN, and Mask-RCNN were popularly used, typically based on ResNet or VGG base architectures. Watershed segmentation on predicted semantic segmentation maps was a popular post-processing strategy. Several of the top techniques compared favorably to an individual human annotator and can be used with confidence for nuclear morphometrics.
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| 38. |
- Li, Quan, et al.
(författare)
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Diagnostic performance of CT-derived resting distal to aortic pressure ratio (resting Pd/Pa) vs. CT-derived fractional flow reserve (CT-FFR) in coronary lesion severity assessment
- 2021
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Ingår i: Annals of Translational Medicine. - : AME Publishing Company. - 2305-5839 .- 2305-5847. ; 9:17
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Tidskriftsartikel (refereegranskat)abstract
- Background: Computed tomography-derived fractional flow reserve (CT-FFR) has emerged as a promising non-invasive substitute for fractional flow reserve (FFR) measurement. Normally, CT-FFR providing functional significance of coronary artery disease (CAD) by using a simplified total coronary resistance index (TCRI) model. Yet the error or discrepancy caused by this simplified model remains unclear. Methods: A total of 20 consecutive patients with suspected CAD who underwent CTA and invasive FFR measurement were retrospectively analyzed. CT-FFR and CT-(Pd/Pa)rest values derived from the coronary CTA images. The diagnostic performance of CT-FFR and CT-(Pd/Pa)rest were evaluated on a per-vessel level using C statistics with invasive FFR<0.80 as the reference standard. Results: Of the 25 vessels eventually analyzed, the prevalence of functionally significant CAD were 64%. The Youden index of the ROC curve indicated that the best cutoff value of invasive resting Pd/Pa was 0.945 for identifying functionally significant lesions. Sensitivity, specificity, negative predictive value, positive predictive value and accuracy were 85%, 91%, 92%, 83% and 88% for CT-(Pd/Pa)rest and 85%, 58% 69%, 78% and 72% for CT-FFR. Area under the receiver-operating characteristic curve (AUC) to detect functionally significant stenoses of CT-(Pd/Pa)rest and CT-FFR were 0.87 and 0.90. Conclusions: In this study, the results suggest CT-derived resting Pd/Pa has a potential advantage over CT-FFR in triaging patients for revascularization.
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| 39. |
- Lidayová, Kristína, et al.
(författare)
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Coverage segmentation of 3D thin structures
- 2015
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Ingår i: Image Processing Theory, Tools and Applications (IPTA), 2015 International Conference on. - Piscataway, NJ : IEEE conference proceedings. - 9781479986361 ; , s. 23-28
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Konferensbidrag (refereegranskat)abstract
- We present a coverage segmentation method for extracting thin structures in three-dimensional images. The proposed method is an improved extension of our coverage segmentation method for 2D thin structures. We suggest implementation that enables low memory consumption and processing time, and by that applicability of the method on real CTA data. The method needs a reliable crisp segmentation as an input and uses information from linear unmixing and the crisp segmentation to create a high-resolution crisp reconstruction of the object, which can then be used as a final result, or down-sampled to a coverage segmentation at the starting image resolution. Performed quantitative and qualitative analysis confirm excellent performance of the proposed method, both on synthetic and on real data, in particular in terms of robustness to noise.
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| 40. |
- Lidayová, Kristína, et al.
(författare)
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Fast vascular skeleton extraction algorithm
- 2016
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Ingår i: Pattern Recognition Letters. - : Elsevier. - 0167-8655 .- 1872-7344. ; 76, s. 67-75
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Tidskriftsartikel (refereegranskat)abstract
- Vascular diseases are a common cause of death, particularly in developed countries. Computerized image analysis tools play a potentially important role in diagnosing and quantifying vascular pathologies. Given the size and complexity of modern angiographic data acquisition, fast, automatic and accurate vascular segmentation is a challenging task.In this paper we introduce a fully automatic high-speed vascular skeleton extraction algorithm that is intended as a first step in a complete vascular tree segmentation program. The method takes a 3D unprocessed Computed Tomography Angiography (CTA) scan as input and produces a graph in which the nodes are centrally located artery voxels and the edges represent connections between them. The algorithm works in two passes where the first pass is designed to extract the skeleton of large arteries and the second pass focuses on smaller vascular structures. Each pass consists of three main steps. The first step sets proper parameters automatically using Gaussian curve fitting. In the second step different filters are applied to detect voxels - nodes - that are part of arteries. In the last step the nodes are connected in order to obtain a continuous centerline tree for the entire vasculature. Structures found, that do not belong to the arteries, are removed in a final anatomy-based analysis. The proposed method is computationally efficient with an average execution time of 29s and has been tested on a set of CTA scans of the lower limbs achieving an average overlap rate of 97% and an average detection rate of 71%.
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