79 research outputs found

    Active contour driven by scalable local regional information on expandable kernel

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    An active contour that uses the pixelโ€™s intensity on a set of expandable kernels along the propagating contour for image segmentation is presented in this paper. The objective is this study is to employ the scalable kernels to attract the contour to meet the desired boundary. The key characteristics of this scheme is that the kernels gradually expand to find an objectโ€™s boundary. So this scheme could penetrate to the concave boundary more effective and efficient than some other schemes. If a Gaussian kernel is applied, it could trace the object with a blurred or smooth boundary. Moreover, the directional selectivity feature enables in capturing two edgeโ€™s types with just one initial position. Its performance showed more desirable segmentation outcomes compared to the other existing active contours using regional information when segmenting the noisy image and the non-uniform (or heterogeneous) textures. Meanwhile, the level set implementation enables topological flexibility to our active contour scheme

    Automated Segmentation of Cerebral Aneurysm Using a Novel Statistical Multiresolution Approach

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    Cerebral Aneurysm (CA) is a vascular disease that threatens the lives of many adults. It a ects almost 1:5 - 5% of the general population. Sub- Arachnoid Hemorrhage (SAH), resulted by a ruptured CA, has high rates of morbidity and mortality. Therefore, radiologists aim to detect it and diagnose it at an early stage, by analyzing the medical images, to prevent or reduce its damages. The analysis process is traditionally done manually. However, with the emerging of the technology, Computer-Aided Diagnosis (CAD) algorithms are adopted in the clinics to overcome the traditional process disadvantages, as the dependency of the radiologist's experience, the inter and intra observation variability, the increase in the probability of error which increases consequently with the growing number of medical images to be analyzed, and the artifacts added by the medical images' acquisition methods (i.e., MRA, CTA, PET, RA, etc.) which impedes the radiologist' s work. Due to the aforementioned reasons, many research works propose di erent segmentation approaches to automate the analysis process of detecting a CA using complementary segmentation techniques; but due to the challenging task of developing a robust reproducible reliable algorithm to detect CA regardless of its shape, size, and location from a variety of the acquisition methods, a diversity of proposed and developed approaches exist which still su er from some limitations. This thesis aims to contribute in this research area by adopting two promising techniques based on the multiresolution and statistical approaches in the Two-Dimensional (2D) domain. The rst technique is the Contourlet Transform (CT), which empowers the segmentation by extracting features not apparent in the normal image scale. While the second technique is the Hidden Markov Random Field model with Expectation Maximization (HMRF-EM), which segments the image based on the relationship of the neighboring pixels in the contourlet domain. The developed algorithm reveals promising results on the four tested Three- Dimensional Rotational Angiography (3D RA) datasets, where an objective and a subjective evaluation are carried out. For the objective evaluation, six performance metrics are adopted which are: accuracy, Dice Similarity Index (DSI), False Positive Ratio (FPR), False Negative Ratio (FNR), speci city, and sensitivity. As for the subjective evaluation, one expert and four observers with some medical background are involved to assess the segmentation visually. Both evaluations compare the segmented volumes against the ground truth data

    Improved graph cut model with features of superpixels and neighborhood patches for myocardium segmentation from ultrasound image

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    Ultrasound (US) imaging has the technical advantages for the functional evaluation of myocardium compared with other imaging modalities. However, it is a challenge of extracting the myocardial tissues from the background due to low quality of US imaging. To better extract the myocardial tissues, this study proposes a semi-supervised segmentation method of fast Superpixels and Neighborhood Patches based Continuous Min-Cut (fSP-CMC). The US image is represented by a graph, which is constructed depending on the features of superpixels and neighborhood patches

    Semi-supervised segmentation of ultrasound images based on patch representation and continuous min cut.

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    Ultrasound segmentation is a challenging problem due to the inherent speckle and some artifacts like shadows, attenuation and signal dropout. Existing methods need to include strong priors like shape priors or analytical intensity models to succeed in the segmentation. However, such priors tend to limit these methods to a specific target or imaging settings, and they are not always applicable to pathological cases. This work introduces a semi-supervised segmentation framework for ultrasound imaging that alleviates the limitation of fully automatic segmentation, that is, it is applicable to any kind of target and imaging settings. Our methodology uses a graph of image patches to represent the ultrasound image and user-assisted initialization with labels, which acts as soft priors. The segmentation problem is formulated as a continuous minimum cut problem and solved with an efficient optimization algorithm. We validate our segmentation framework on clinical ultrasound imaging (prostate, fetus, and tumors of the liver and eye). We obtain high similarity agreement with the ground truth provided by medical expert delineations in all applications (94% DICE values in average) and the proposed algorithm performs favorably with the literature

    ๋ณต๋ถ€ CT์—์„œ ๊ฐ„๊ณผ ํ˜ˆ๊ด€ ๋ถ„ํ•  ๊ธฐ๋ฒ•

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    ํ•™์œ„๋…ผ๋ฌธ(๋ฐ•์‚ฌ)--์„œ์šธ๋Œ€ํ•™๊ต ๋Œ€ํ•™์› :๊ณต๊ณผ๋Œ€ํ•™ ์ปดํ“จํ„ฐ๊ณตํ•™๋ถ€,2020. 2. ์‹ ์˜๊ธธ.๋ณต๋ถ€ ์ „์‚ฐํ™” ๋‹จ์ธต ์ดฌ์˜ (CT) ์˜์ƒ์—์„œ ์ •ํ™•ํ•œ ๊ฐ„ ๋ฐ ํ˜ˆ๊ด€ ๋ถ„ํ• ์€ ์ฒด์  ์ธก์ •, ์น˜๋ฃŒ ๊ณ„ํš ์ˆ˜๋ฆฝ ๋ฐ ์ถ”๊ฐ€์ ์ธ ์ฆ๊ฐ• ํ˜„์‹ค ๊ธฐ๋ฐ˜ ์ˆ˜์ˆ  ๊ฐ€์ด๋“œ์™€ ๊ฐ™์€ ์ปดํ“จํ„ฐ ์ง„๋‹จ ๋ณด์กฐ ์‹œ์Šคํ…œ์„ ๊ตฌ์ถ•ํ•˜๋Š”๋ฐ ํ•„์ˆ˜์ ์ธ ์š”์†Œ์ด๋‹ค. ์ตœ๊ทผ ๋“ค์–ด ์ปจ๋ณผ๋ฃจ์…”๋„ ์ธ๊ณต ์‹ ๊ฒฝ๋ง (CNN) ํ˜•ํƒœ์˜ ๋”ฅ ๋Ÿฌ๋‹์ด ๋งŽ์ด ์ ์šฉ๋˜๋ฉด์„œ ์˜๋ฃŒ ์˜์ƒ ๋ถ„ํ• ์˜ ์„ฑ๋Šฅ์ด ํ–ฅ์ƒ๋˜๊ณ  ์žˆ์ง€๋งŒ, ์‹ค์ œ ์ž„์ƒ์— ์ ์šฉํ•  ์ˆ˜ ์žˆ๋Š” ๋†’์€ ์ผ๋ฐ˜ํ™” ์„ฑ๋Šฅ์„ ์ œ๊ณตํ•˜๊ธฐ๋Š” ์—ฌ์ „ํžˆ ์–ด๋ ต๋‹ค. ๋˜ํ•œ ๋ฌผ์ฒด์˜ ๊ฒฝ๊ณ„๋Š” ์ „ํ†ต์ ์œผ๋กœ ์˜์ƒ ๋ถ„ํ• ์—์„œ ๋งค์šฐ ์ค‘์š”ํ•œ ์š”์†Œ๋กœ ์ด์šฉ๋˜์—ˆ์ง€๋งŒ, CT ์˜์ƒ์—์„œ ๊ฐ„์˜ ๋ถˆ๋ถ„๋ช…ํ•œ ๊ฒฝ๊ณ„๋ฅผ ์ถ”์ถœํ•˜๊ธฐ๊ฐ€ ์–ด๋ ต๊ธฐ ๋•Œ๋ฌธ์— ํ˜„๋Œ€ CNN์—์„œ๋Š” ์ด๋ฅผ ์‚ฌ์šฉํ•˜์ง€ ์•Š๊ณ  ์žˆ๋‹ค. ๊ฐ„ ํ˜ˆ๊ด€ ๋ถ„ํ•  ์ž‘์—…์˜ ๊ฒฝ์šฐ, ๋ณต์žกํ•œ ํ˜ˆ๊ด€ ์˜์ƒ์œผ๋กœ๋ถ€ํ„ฐ ํ•™์Šต ๋ฐ์ดํ„ฐ๋ฅผ ๋งŒ๋“ค๊ธฐ ์–ด๋ ต๊ธฐ ๋•Œ๋ฌธ์— ๋”ฅ ๋Ÿฌ๋‹์„ ์ ์šฉํ•˜๊ธฐ๊ฐ€ ์–ด๋ ต๋‹ค. ๋˜ํ•œ ์–‡์€ ํ˜ˆ๊ด€ ๋ถ€๋ถ„์˜ ์˜์ƒ ๋ฐ๊ธฐ ๋Œ€๋น„๊ฐ€ ์•ฝํ•˜์—ฌ ์›๋ณธ ์˜์ƒ์—์„œ ์‹๋ณ„ํ•˜๊ธฐ๊ฐ€ ๋งค์šฐ ์–ด๋ ต๋‹ค. ๋ณธ ๋…ผ๋ฌธ์—์„œ๋Š” ์œ„ ์–ธ๊ธ‰ํ•œ ๋ฌธ์ œ๋“ค์„ ํ•ด๊ฒฐํ•˜๊ธฐ ์œ„ํ•ด ์ผ๋ฐ˜ํ™” ์„ฑ๋Šฅ์ด ํ–ฅ์ƒ๋œ CNN๊ณผ ์–‡์€ ํ˜ˆ๊ด€์„ ํฌํ•จํ•˜๋Š” ๋ณต์žกํ•œ ๊ฐ„ ํ˜ˆ๊ด€์„ ์ •ํ™•ํ•˜๊ฒŒ ๋ถ„ํ• ํ•˜๋Š” ์•Œ๊ณ ๋ฆฌ์ฆ˜์„ ์ œ์•ˆํ•œ๋‹ค. ๊ฐ„ ๋ถ„ํ•  ์ž‘์—…์—์„œ ์šฐ์ˆ˜ํ•œ ์ผ๋ฐ˜ํ™” ์„ฑ๋Šฅ์„ ๊ฐ–๋Š” CNN์„ ๊ตฌ์ถ•ํ•˜๊ธฐ ์œ„ํ•ด, ๋‚ด๋ถ€์ ์œผ๋กœ ๊ฐ„ ๋ชจ์–‘์„ ์ถ”์ •ํ•˜๋Š” ๋ถ€๋ถ„์ด ํฌํ•จ๋œ ์ž๋™ ์ปจํ…์ŠคํŠธ ์•Œ๊ณ ๋ฆฌ์ฆ˜์„ ์ œ์•ˆํ•œ๋‹ค. ๋˜ํ•œ, CNN์„ ์‚ฌ์šฉํ•œ ํ•™์Šต์— ๊ฒฝ๊ณ„์„ ์˜ ๊ฐœ๋…์ด ์ƒˆ๋กญ๊ฒŒ ์ œ์•ˆ๋œ๋‹ค. ๋ชจํ˜ธํ•œ ๊ฒฝ๊ณ„๋ถ€๊ฐ€ ํฌํ•จ๋˜์–ด ์žˆ์–ด ์ „์ฒด ๊ฒฝ๊ณ„ ์˜์—ญ์„ CNN์— ํ›ˆ๋ จํ•˜๋Š” ๊ฒƒ์€ ๋งค์šฐ ์–ด๋ ต๊ธฐ ๋•Œ๋ฌธ์— ๋ฐ˜๋ณต๋˜๋Š” ํ•™์Šต ๊ณผ์ •์—์„œ ์ธ๊ณต ์‹ ๊ฒฝ๋ง์ด ์Šค์Šค๋กœ ์˜ˆ์ธกํ•œ ํ™•๋ฅ ์—์„œ ๋ถ€์ •ํ™•ํ•˜๊ฒŒ ์ถ”์ •๋œ ๋ถ€๋ถ„์  ๊ฒฝ๊ณ„๋งŒ์„ ์‚ฌ์šฉํ•˜์—ฌ ์ธ๊ณต ์‹ ๊ฒฝ๋ง์„ ํ•™์Šตํ•œ๋‹ค. ์‹คํ—˜์  ๊ฒฐ๊ณผ๋ฅผ ํ†ตํ•ด ์ œ์•ˆ๋œ CNN์ด ๋‹ค๋ฅธ ์ตœ์‹  ๊ธฐ๋ฒ•๋“ค๋ณด๋‹ค ์ •ํ™•๋„๊ฐ€ ์šฐ์ˆ˜ํ•˜๋‹ค๋Š” ๊ฒƒ์„ ๋ณด์ธ๋‹ค. ๋˜ํ•œ, ์ œ์•ˆ๋œ CNN์˜ ์ผ๋ฐ˜ํ™” ์„ฑ๋Šฅ์„ ๊ฒ€์ฆํ•˜๊ธฐ ์œ„ํ•ด ๋‹ค์–‘ํ•œ ์‹คํ—˜์„ ์ˆ˜ํ–‰ํ•œ๋‹ค. ๊ฐ„ ํ˜ˆ๊ด€ ๋ถ„ํ• ์—์„œ๋Š” ๊ฐ„ ๋‚ด๋ถ€์˜ ๊ด€์‹ฌ ์˜์—ญ์„ ์ง€์ •ํ•˜๊ธฐ ์œ„ํ•ด ์•ž์„œ ํš๋“ํ•œ ๊ฐ„ ์˜์—ญ์„ ํ™œ์šฉํ•œ๋‹ค. ์ •ํ™•ํ•œ ๊ฐ„ ํ˜ˆ๊ด€ ๋ถ„ํ• ์„ ์œ„ํ•ด ํ˜ˆ๊ด€ ํ›„๋ณด ์ ๋“ค์„ ์ถ”์ถœํ•˜์—ฌ ์‚ฌ์šฉํ•˜๋Š” ์•Œ๊ณ ๋ฆฌ์ฆ˜์„ ์ œ์•ˆํ•œ๋‹ค. ํ™•์‹คํ•œ ํ›„๋ณด ์ ๋“ค์„ ์–ป๊ธฐ ์œ„ํ•ด, ์‚ผ์ฐจ์› ์˜์ƒ์˜ ์ฐจ์›์„ ๋จผ์ € ์ตœ๋Œ€ ๊ฐ•๋„ ํˆฌ์˜ ๊ธฐ๋ฒ•์„ ํ†ตํ•ด ์ด์ฐจ์›์œผ๋กœ ๋‚ฎ์ถ˜๋‹ค. ์ด์ฐจ์› ์˜์ƒ์—์„œ๋Š” ๋ณต์žกํ•œ ํ˜ˆ๊ด€์˜ ๊ตฌ์กฐ๊ฐ€ ๋ณด๋‹ค ๋‹จ์ˆœํ™”๋  ์ˆ˜ ์žˆ๋‹ค. ์ด์–ด์„œ, ์ด์ฐจ์› ์˜์ƒ์—์„œ ํ˜ˆ๊ด€ ๋ถ„ํ• ์„ ์ˆ˜ํ–‰ํ•˜๊ณ  ํ˜ˆ๊ด€ ํ”ฝ์…€๋“ค์€ ์›๋ž˜์˜ ์‚ผ์ฐจ์› ๊ณต๊ฐ„์ƒ์œผ๋กœ ์—ญ ํˆฌ์˜๋œ๋‹ค. ๋งˆ์ง€๋ง‰์œผ๋กœ, ์ „์ฒด ํ˜ˆ๊ด€์˜ ๋ถ„ํ• ์„ ์œ„ํ•ด ์›๋ณธ ์˜์ƒ๊ณผ ํ˜ˆ๊ด€ ํ›„๋ณด ์ ๋“ค์„ ๋ชจ๋‘ ์‚ฌ์šฉํ•˜๋Š” ์ƒˆ๋กœ์šด ๋ ˆ๋ฒจ ์…‹ ๊ธฐ๋ฐ˜ ์•Œ๊ณ ๋ฆฌ์ฆ˜์„ ์ œ์•ˆํ•œ๋‹ค. ์ œ์•ˆ๋œ ์•Œ๊ณ ๋ฆฌ์ฆ˜์€ ๋ณต์žกํ•œ ๊ตฌ์กฐ๊ฐ€ ๋‹จ์ˆœํ™”๋˜๊ณ  ์–‡์€ ํ˜ˆ๊ด€์ด ๋” ์ž˜ ๋ณด์ด๋Š” ์ด์ฐจ์› ์˜์ƒ์—์„œ ์–ป์€ ํ›„๋ณด ์ ๋“ค์„ ์‚ฌ์šฉํ•˜๊ธฐ ๋•Œ๋ฌธ์— ์–‡์€ ํ˜ˆ๊ด€ ๋ถ„ํ• ์—์„œ ๋†’์€ ์ •ํ™•๋„๋ฅผ ๋ณด์ธ๋‹ค. ์‹คํ—˜์  ๊ฒฐ๊ณผ์— ์˜ํ•˜๋ฉด ์ œ์•ˆ๋œ ์•Œ๊ณ ๋ฆฌ์ฆ˜์€ ์ž˜๋ชป๋œ ์˜์—ญ์˜ ์ถ”์ถœ ์—†์ด ๋‹ค๋ฅธ ๋ ˆ๋ฒจ ์…‹ ๊ธฐ๋ฐ˜ ์•Œ๊ณ ๋ฆฌ์ฆ˜๋“ค๋ณด๋‹ค ์šฐ์ˆ˜ํ•œ ์„ฑ๋Šฅ์„ ๋ณด์ธ๋‹ค. ์ œ์•ˆ๋œ ์•Œ๊ณ ๋ฆฌ์ฆ˜์€ ๊ฐ„๊ณผ ํ˜ˆ๊ด€์„ ๋ถ„ํ• ํ•˜๋Š” ์ƒˆ๋กœ์šด ๋ฐฉ๋ฒ•์„ ์ œ์‹œํ•œ๋‹ค. ์ œ์•ˆ๋œ ์ž๋™ ์ปจํ…์ŠคํŠธ ๊ตฌ์กฐ๋Š” ์‚ฌ๋žŒ์ด ๋””์ž์ธํ•œ ํ•™์Šต ๊ณผ์ •์ด ์ผ๋ฐ˜ํ™” ์„ฑ๋Šฅ์„ ํฌ๊ฒŒ ํ–ฅ์ƒํ•  ์ˆ˜ ์žˆ๋‹ค๋Š” ๊ฒƒ์„ ๋ณด์ธ๋‹ค. ๊ทธ๋ฆฌ๊ณ  ์ œ์•ˆ๋œ ๊ฒฝ๊ณ„์„  ํ•™์Šต ๊ธฐ๋ฒ•์œผ๋กœ CNN์„ ์‚ฌ์šฉํ•œ ์˜์ƒ ๋ถ„ํ• ์˜ ์„ฑ๋Šฅ์„ ํ–ฅ์ƒํ•  ์ˆ˜ ์žˆ์Œ์„ ๋‚ดํฌํ•œ๋‹ค. ๊ฐ„ ํ˜ˆ๊ด€์˜ ๋ถ„ํ• ์€ ์ด์ฐจ์› ์ตœ๋Œ€ ๊ฐ•๋„ ํˆฌ์˜ ๊ธฐ๋ฐ˜ ์ด๋ฏธ์ง€๋กœ๋ถ€ํ„ฐ ํš๋“๋œ ํ˜ˆ๊ด€ ํ›„๋ณด ์ ๋“ค์„ ํ†ตํ•ด ์–‡์€ ํ˜ˆ๊ด€๋“ค์ด ์„ฑ๊ณต์ ์œผ๋กœ ๋ถ„ํ• ๋  ์ˆ˜ ์žˆ์Œ์„ ๋ณด์ธ๋‹ค. ๋ณธ ๋…ผ๋ฌธ์—์„œ ์ œ์•ˆ๋œ ์•Œ๊ณ ๋ฆฌ์ฆ˜์€ ๊ฐ„์˜ ํ•ด๋ถ€ํ•™์  ๋ถ„์„๊ณผ ์ž๋™ํ™”๋œ ์ปดํ“จํ„ฐ ์ง„๋‹จ ๋ณด์กฐ ์‹œ์Šคํ…œ์„ ๊ตฌ์ถ•ํ•˜๋Š” ๋ฐ ๋งค์šฐ ์ค‘์š”ํ•œ ๊ธฐ์ˆ ์ด๋‹ค.Accurate liver and its vessel segmentation on abdominal computed tomography (CT) images is one of the most important prerequisites for computer-aided diagnosis (CAD) systems such as volumetric measurement, treatment planning, and further augmented reality-based surgical guide. In recent years, the application of deep learning in the form of convolutional neural network (CNN) has improved the performance of medical image segmentation, but it is difficult to provide high generalization performance for the actual clinical practice. Furthermore, although the contour features are an important factor in the image segmentation problem, they are hard to be employed on CNN due to many unclear boundaries on the image. In case of a liver vessel segmentation, a deep learning approach is impractical because it is difficult to obtain training data from complex vessel images. Furthermore, thin vessels are hard to be identified in the original image due to weak intensity contrasts and noise. In this dissertation, a CNN with high generalization performance and a contour learning scheme is first proposed for liver segmentation. Secondly, a liver vessel segmentation algorithm is presented that accurately segments even thin vessels. To build a CNN with high generalization performance, the auto-context algorithm is employed. The auto-context algorithm goes through two pipelines: the first predicts the overall area of a liver and the second predicts the final liver using the first prediction as a prior. This process improves generalization performance because the network internally estimates shape-prior. In addition to the auto-context, a contour learning method is proposed that uses only sparse contours rather than the entire contour. Sparse contours are obtained and trained by using only the mispredicted part of the network's final prediction. Experimental studies show that the proposed network is superior in accuracy to other modern networks. Multiple N-fold tests are also performed to verify the generalization performance. An algorithm for accurate liver vessel segmentation is also proposed by introducing vessel candidate points. To obtain confident vessel candidates, the 3D image is first reduced to 2D through maximum intensity projection. Subsequently, vessel segmentation is performed from the 2D images and the segmented pixels are back-projected into the original 3D space. Finally, a new level set function is proposed that utilizes both the original image and vessel candidate points. The proposed algorithm can segment thin vessels with high accuracy by mainly using vessel candidate points. The reliability of the points can be higher through robust segmentation in the projected 2D images where complex structures are simplified and thin vessels are more visible. Experimental results show that the proposed algorithm is superior to other active contour models. The proposed algorithms present a new method of segmenting the liver and its vessels. The auto-context algorithm shows that a human-designed curriculum (i.e., shape-prior learning) can improve generalization performance. The proposed contour learning technique can increase the accuracy of a CNN for image segmentation by focusing on its failures, represented by sparse contours. The vessel segmentation shows that minor vessel branches can be successfully segmented through vessel candidate points obtained by reducing the image dimension. The algorithms presented in this dissertation can be employed for later analysis of liver anatomy that requires accurate segmentation techniques.Chapter 1 Introduction 1 1.1 Background and motivation 1 1.2 Problem statement 3 1.3 Main contributions 6 1.4 Contents and organization 9 Chapter 2 Related Works 10 2.1 Overview 10 2.2 Convolutional neural networks 11 2.2.1 Architectures of convolutional neural networks 11 2.2.2 Convolutional neural networks in medical image segmentation 21 2.3 Liver and vessel segmentation 37 2.3.1 Classical methods for liver segmentation 37 2.3.2 Vascular image segmentation 40 2.3.3 Active contour models 46 2.3.4 Vessel topology-based active contour model 54 2.4 Motivation 60 Chapter 3 Liver Segmentation via Auto-Context Neural Network with Self-Supervised Contour Attention 62 3.1 Overview 62 3.2 Single-pass auto-context neural network 65 3.2.1 Skip-attention module 66 3.2.2 V-transition module 69 3.2.3 Liver-prior inference and auto-context 70 3.2.4 Understanding the network 74 3.3 Self-supervising contour attention 75 3.4 Learning the network 81 3.4.1 Overall loss function 81 3.4.2 Data augmentation 81 3.5 Experimental Results 83 3.5.1 Overview 83 3.5.2 Data configurations and target of comparison 84 3.5.3 Evaluation metric 85 3.5.4 Accuracy evaluation 87 3.5.5 Ablation study 93 3.5.6 Performance of generalization 110 3.5.7 Results from ground-truth variations 114 3.6 Discussion 116 Chapter 4 Liver Vessel Segmentation via Active Contour Model with Dense Vessel Candidates 119 4.1 Overview 119 4.2 Dense vessel candidates 124 4.2.1 Maximum intensity slab images 125 4.2.2 Segmentation of 2D vessel candidates and back-projection 130 4.3 Clustering of dense vessel candidates 135 4.3.1 Virtual gradient-assisted regional ACM 136 4.3.2 Localized regional ACM 142 4.4 Experimental results 145 4.4.1 Overview 145 4.4.2 Data configurations and environment 146 4.4.3 2D segmentation 146 4.4.4 ACM comparisons 149 4.4.5 Evaluation of bifurcation points 154 4.4.6 Computational performance 159 4.4.7 Ablation study 160 4.4.8 Parameter study 162 4.5 Application to portal vein analysis 164 4.6 Discussion 168 Chapter 5 Conclusion and Future Works 170 Bibliography 172 ์ดˆ๋ก 197Docto

    Level Set Methods for MRE Image Processing and Analysis

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    Ph.DDOCTOR OF PHILOSOPH

    Nephroblastoma in MRI Data

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    The main objective of this work is the mathematical analysis of nephroblastoma in MRI sequences. At the beginning we provide two different datasets for segmentation and classification. Based on the first dataset, we analyze the current clinical practice regarding therapy planning on the basis of annotations of a single radiologist. We can show with our benchmark that this approach is not optimal and that there may be significant differences between human annotators and even radiologists. In addition, we demonstrate that the approximation of the tumor shape currently used is too coarse granular and thus prone to errors. We address this problem and develop a method for interactive segmentation that allows an intuitive and accurate annotation of the tumor. While the first part of this thesis is mainly concerned with the segmentation of Wilmsโ€™ tumors, the second part deals with the reliability of diagnosis and the planning of the course of therapy. The second data set we compiled allows us to develop a method that dramatically improves the differential diagnosis between nephroblastoma and its precursor lesion nephroblastomatosis. Finally, we can show that even the standard MRI modality for Wilmsโ€™ tumors is sufficient to estimate the developmental tendencies of nephroblastoma under chemotherapy
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