903 research outputs found
Automated liver tissues delineation based on machine learning techniques: A survey, current trends and future orientations
There is no denying how machine learning and computer vision have grown in
the recent years. Their highest advantages lie within their automation,
suitability, and ability to generate astounding results in a matter of seconds
in a reproducible manner. This is aided by the ubiquitous advancements reached
in the computing capabilities of current graphical processing units and the
highly efficient implementation of such techniques. Hence, in this paper, we
survey the key studies that are published between 2014 and 2020, showcasing the
different machine learning algorithms researchers have used to segment the
liver, hepatic-tumors, and hepatic-vasculature structures. We divide the
surveyed studies based on the tissue of interest (hepatic-parenchyma,
hepatic-tumors, or hepatic-vessels), highlighting the studies that tackle more
than one task simultaneously. Additionally, the machine learning algorithms are
classified as either supervised or unsupervised, and further partitioned if the
amount of works that fall under a certain scheme is significant. Moreover,
different datasets and challenges found in literature and websites, containing
masks of the aforementioned tissues, are thoroughly discussed, highlighting the
organizers original contributions, and those of other researchers. Also, the
metrics that are used excessively in literature are mentioned in our review
stressing their relevancy to the task at hand. Finally, critical challenges and
future directions are emphasized for innovative researchers to tackle, exposing
gaps that need addressing such as the scarcity of many studies on the vessels
segmentation challenge, and why their absence needs to be dealt with in an
accelerated manner.Comment: 41 pages, 4 figures, 13 equations, 1 table. A review paper on liver
tissues segmentation based on automated ML-based technique
Dynamical models and machine learning for supervised segmentation
This thesis is concerned with the problem of how to outline regions of interest in medical images, when
the boundaries are weak or ambiguous and the region shapes are irregular. The focus on machine learning
and interactivity leads to a common theme of the need to balance conflicting requirements. First,
any machine learning method must strike a balance between how much it can learn and how well it
generalises. Second, interactive methods must balance minimal user demand with maximal user control.
To address the problem of weak boundaries,methods of supervised texture classification are investigated
that do not use explicit texture features. These methods enable prior knowledge about the image to
benefit any segmentation framework. A chosen dynamic contour model, based on probabilistic boundary
tracking, combines these image priors with efficient modes of interaction. We show the benefits of the
texture classifiers over intensity and gradient-based image models, in both classification and boundary
extraction.
To address the problem of irregular region shape, we devise a new type of statistical shape model
(SSM) that does not use explicit boundary features or assume high-level similarity between region
shapes. First, the models are used for shape discrimination, to constrain any segmentation framework
by way of regularisation. Second, the SSMs are used for shape generation, allowing probabilistic segmentation
frameworks to draw shapes from a prior distribution. The generative models also include
novel methods to constrain shape generation according to information from both the image and user
interactions.
The shape models are first evaluated in terms of discrimination capability, and shown to out-perform
other shape descriptors. Experiments also show that the shape models can benefit a standard type of
segmentation algorithm by providing shape regularisers. We finally show how to exploit the shape
models in supervised segmentation frameworks, and evaluate their benefits in user trials
่ น้จCTๅไธใฎ่คๆฐใชใใธใงใฏใใฎใปใฐใกใณใใผใทใงใณใฎใใใฎ็ตฑ่จ็ๆๆณใซ้ขใใ็ ็ฉถ
Computer aided diagnosis (CAD) is the use of a computer-generated output as an auxiliary tool for the assistance of efficient interpretation and accurate diagnosis. Medical image segmentation has an essential role in CAD in clinical applications. Generally, the task of medical image segmentation involves multiple objects, such as organs or diffused tumor regions. Moreover, it is very unfavorable to segment these regions from abdominal Computed Tomography (CT) images because of the overlap in intensity and variability in position and shape of soft tissues. In this thesis, a progressive segmentation framework is proposed to extract liver and tumor regions from CT images more efficiently, which includes the steps of multiple organs coarse segmentation, fine segmentation, and liver tumors segmentation. Benefit from the previous knowledge of the shape and its deformation, the Statistical shape model (SSM) method is firstly utilized to segment multiple organs regions robustly. In the process of building an SSM, the correspondence of landmarks is crucial to the quality of the model. To generate a more representative prototype of organ surface, a k-mean clustering method is proposed. The quality of the SSMs, which is measured by generalization ability, specificity, and compactness, was improved. We furtherly extend the shapes correspondence to multiple objects. A non-rigid iterative closest point surface registration process is proposed to seek more properly corresponded landmarks across the multi-organ surfaces. The accuracy of surface registration was improved as well as the model quality. Moreover, to localize the abdominal organs simultaneously, we proposed a random forest regressor cooperating intensity features to predict the position of multiple organs in the CT image. The regions of the organs are substantially restrained using the trained shape models. The accuracy of coarse segmentation using SSMs was increased by the initial information of organ positions.Consequently, a pixel-wise segmentation using the classification of supervoxels is applied for the fine segmentation of multiple organs. The intensity and spatial features are extracted from each supervoxels and classified by a trained random forest. The boundary of the supervoxels is closer to the real organs than the previous coarse segmentation. Finally, we developed a hybrid framework for liver tumor segmentation in multiphase images. To deal with these issues of distinguishing and delineating tumor regions and peripheral tissues, this task is accomplished in two steps: a cascade region-based convolutional neural network (R-CNN) with a refined head is trained to locate the bounding boxes that contain tumors, and a phase-sensitive noise filtering is introduced to refine the following segmentation of tumor regions conducted by a level-set-based framework. The results of tumor detection show the adjacent tumors are successfully separated by the improved cascaded R-CNN. The accuracy of tumor segmentation is also improved by our proposed method. 26 cases of multi-phase CT images were used to validate our proposed method for the segmentation of liver tumors. The average precision and recall rates for tumor detection are 76.8% and 84.4%, respectively. The intersection over union, true positive rate, and false positive rate for tumor segmentation are 72.7%, 76.2%, and 4.75%, respectively.ไนๅทๅทฅๆฅญๅคงๅญฆๅๅฃซๅญฆไฝ่ซๆ ๅญฆไฝ่จ็ชๅท: ๅทฅๅ็ฒ็ฌฌ546ๅท ๅญฆไฝๆไธๅนดๆๆฅ: ไปคๅ4ๅนด3ๆ25ๆฅ1 Introduction|2 Literature Review|3 Statistical Shape Model Building|4 Multi-organ Segmentation|5 Liver Tumors Segmentation|6 Summary and Outlookไนๅทๅทฅๆฅญๅคงๅญฆไปคๅ3ๅนด
๋ณต๋ถ CT์์ ๊ฐ๊ณผ ํ๊ด ๋ถํ ๊ธฐ๋ฒ
ํ์๋
ผ๋ฌธ(๋ฐ์ฌ)--์์ธ๋ํ๊ต ๋ํ์ :๊ณต๊ณผ๋ํ ์ปดํจํฐ๊ณตํ๋ถ,2020. 2. ์ ์๊ธธ.๋ณต๋ถ ์ ์ฐํ ๋จ์ธต ์ดฌ์ (CT) ์์์์ ์ ํํ ๊ฐ ๋ฐ ํ๊ด ๋ถํ ์ ์ฒด์ ์ธก์ , ์น๋ฃ ๊ณํ ์๋ฆฝ ๋ฐ ์ถ๊ฐ์ ์ธ ์ฆ๊ฐ ํ์ค ๊ธฐ๋ฐ ์์ ๊ฐ์ด๋์ ๊ฐ์ ์ปดํจํฐ ์ง๋จ ๋ณด์กฐ ์์คํ
์ ๊ตฌ์ถํ๋๋ฐ ํ์์ ์ธ ์์์ด๋ค. ์ต๊ทผ ๋ค์ด ์ปจ๋ณผ๋ฃจ์
๋ ์ธ๊ณต ์ ๊ฒฝ๋ง (CNN) ํํ์ ๋ฅ ๋ฌ๋์ด ๋ง์ด ์ ์ฉ๋๋ฉด์ ์๋ฃ ์์ ๋ถํ ์ ์ฑ๋ฅ์ด ํฅ์๋๊ณ ์์ง๋ง, ์ค์ ์์์ ์ ์ฉํ ์ ์๋ ๋์ ์ผ๋ฐํ ์ฑ๋ฅ์ ์ ๊ณตํ๊ธฐ๋ ์ฌ์ ํ ์ด๋ ต๋ค. ๋ํ ๋ฌผ์ฒด์ ๊ฒฝ๊ณ๋ ์ ํต์ ์ผ๋ก ์์ ๋ถํ ์์ ๋งค์ฐ ์ค์ํ ์์๋ก ์ด์ฉ๋์์ง๋ง, CT ์์์์ ๊ฐ์ ๋ถ๋ถ๋ช
ํ ๊ฒฝ๊ณ๋ฅผ ์ถ์ถํ๊ธฐ๊ฐ ์ด๋ ต๊ธฐ ๋๋ฌธ์ ํ๋ CNN์์๋ ์ด๋ฅผ ์ฌ์ฉํ์ง ์๊ณ ์๋ค. ๊ฐ ํ๊ด ๋ถํ ์์
์ ๊ฒฝ์ฐ, ๋ณต์กํ ํ๊ด ์์์ผ๋ก๋ถํฐ ํ์ต ๋ฐ์ดํฐ๋ฅผ ๋ง๋ค๊ธฐ ์ด๋ ต๊ธฐ ๋๋ฌธ์ ๋ฅ ๋ฌ๋์ ์ ์ฉํ๊ธฐ๊ฐ ์ด๋ ต๋ค. ๋ํ ์์ ํ๊ด ๋ถ๋ถ์ ์์ ๋ฐ๊ธฐ ๋๋น๊ฐ ์ฝํ์ฌ ์๋ณธ ์์์์ ์๋ณํ๊ธฐ๊ฐ ๋งค์ฐ ์ด๋ ต๋ค. ๋ณธ ๋
ผ๋ฌธ์์๋ ์ ์ธ๊ธํ ๋ฌธ์ ๋ค์ ํด๊ฒฐํ๊ธฐ ์ํด ์ผ๋ฐํ ์ฑ๋ฅ์ด ํฅ์๋ CNN๊ณผ ์์ ํ๊ด์ ํฌํจํ๋ ๋ณต์กํ ๊ฐ ํ๊ด์ ์ ํํ๊ฒ ๋ถํ ํ๋ ์๊ณ ๋ฆฌ์ฆ์ ์ ์ํ๋ค.
๊ฐ ๋ถํ ์์
์์ ์ฐ์ํ ์ผ๋ฐํ ์ฑ๋ฅ์ ๊ฐ๋ CNN์ ๊ตฌ์ถํ๊ธฐ ์ํด, ๋ด๋ถ์ ์ผ๋ก ๊ฐ ๋ชจ์์ ์ถ์ ํ๋ ๋ถ๋ถ์ด ํฌํจ๋ ์๋ ์ปจํ
์คํธ ์๊ณ ๋ฆฌ์ฆ์ ์ ์ํ๋ค. ๋ํ, CNN์ ์ฌ์ฉํ ํ์ต์ ๊ฒฝ๊ณ์ ์ ๊ฐ๋
์ด ์๋กญ๊ฒ ์ ์๋๋ค. ๋ชจํธํ ๊ฒฝ๊ณ๋ถ๊ฐ ํฌํจ๋์ด ์์ด ์ ์ฒด ๊ฒฝ๊ณ ์์ญ์ CNN์ ํ๋ จํ๋ ๊ฒ์ ๋งค์ฐ ์ด๋ ต๊ธฐ ๋๋ฌธ์ ๋ฐ๋ณต๋๋ ํ์ต ๊ณผ์ ์์ ์ธ๊ณต ์ ๊ฒฝ๋ง์ด ์ค์ค๋ก ์์ธกํ ํ๋ฅ ์์ ๋ถ์ ํํ๊ฒ ์ถ์ ๋ ๋ถ๋ถ์ ๊ฒฝ๊ณ๋ง์ ์ฌ์ฉํ์ฌ ์ธ๊ณต ์ ๊ฒฝ๋ง์ ํ์ตํ๋ค. ์คํ์ ๊ฒฐ๊ณผ๋ฅผ ํตํด ์ ์๋ CNN์ด ๋ค๋ฅธ ์ต์ ๊ธฐ๋ฒ๋ค๋ณด๋ค ์ ํ๋๊ฐ ์ฐ์ํ๋ค๋ ๊ฒ์ ๋ณด์ธ๋ค. ๋ํ, ์ ์๋ CNN์ ์ผ๋ฐํ ์ฑ๋ฅ์ ๊ฒ์ฆํ๊ธฐ ์ํด ๋ค์ํ ์คํ์ ์ํํ๋ค.
๊ฐ ํ๊ด ๋ถํ ์์๋ ๊ฐ ๋ด๋ถ์ ๊ด์ฌ ์์ญ์ ์ง์ ํ๊ธฐ ์ํด ์์ ํ๋ํ ๊ฐ ์์ญ์ ํ์ฉํ๋ค. ์ ํํ ๊ฐ ํ๊ด ๋ถํ ์ ์ํด ํ๊ด ํ๋ณด ์ ๋ค์ ์ถ์ถํ์ฌ ์ฌ์ฉํ๋ ์๊ณ ๋ฆฌ์ฆ์ ์ ์ํ๋ค. ํ์คํ ํ๋ณด ์ ๋ค์ ์ป๊ธฐ ์ํด, ์ผ์ฐจ์ ์์์ ์ฐจ์์ ๋จผ์ ์ต๋ ๊ฐ๋ ํฌ์ ๊ธฐ๋ฒ์ ํตํด ์ด์ฐจ์์ผ๋ก ๋ฎ์ถ๋ค. ์ด์ฐจ์ ์์์์๋ ๋ณต์กํ ํ๊ด์ ๊ตฌ์กฐ๊ฐ ๋ณด๋ค ๋จ์ํ๋ ์ ์๋ค. ์ด์ด์, ์ด์ฐจ์ ์์์์ ํ๊ด ๋ถํ ์ ์ํํ๊ณ ํ๊ด ํฝ์
๋ค์ ์๋์ ์ผ์ฐจ์ ๊ณต๊ฐ์์ผ๋ก ์ญ ํฌ์๋๋ค. ๋ง์ง๋ง์ผ๋ก, ์ ์ฒด ํ๊ด์ ๋ถํ ์ ์ํด ์๋ณธ ์์๊ณผ ํ๊ด ํ๋ณด ์ ๋ค์ ๋ชจ๋ ์ฌ์ฉํ๋ ์๋ก์ด ๋ ๋ฒจ ์
๊ธฐ๋ฐ ์๊ณ ๋ฆฌ์ฆ์ ์ ์ํ๋ค. ์ ์๋ ์๊ณ ๋ฆฌ์ฆ์ ๋ณต์กํ ๊ตฌ์กฐ๊ฐ ๋จ์ํ๋๊ณ ์์ ํ๊ด์ด ๋ ์ ๋ณด์ด๋ ์ด์ฐจ์ ์์์์ ์ป์ ํ๋ณด ์ ๋ค์ ์ฌ์ฉํ๊ธฐ ๋๋ฌธ์ ์์ ํ๊ด ๋ถํ ์์ ๋์ ์ ํ๋๋ฅผ ๋ณด์ธ๋ค. ์คํ์ ๊ฒฐ๊ณผ์ ์ํ๋ฉด ์ ์๋ ์๊ณ ๋ฆฌ์ฆ์ ์๋ชป๋ ์์ญ์ ์ถ์ถ ์์ด ๋ค๋ฅธ ๋ ๋ฒจ ์
๊ธฐ๋ฐ ์๊ณ ๋ฆฌ์ฆ๋ค๋ณด๋ค ์ฐ์ํ ์ฑ๋ฅ์ ๋ณด์ธ๋ค.
์ ์๋ ์๊ณ ๋ฆฌ์ฆ์ ๊ฐ๊ณผ ํ๊ด์ ๋ถํ ํ๋ ์๋ก์ด ๋ฐฉ๋ฒ์ ์ ์ํ๋ค. ์ ์๋ ์๋ ์ปจํ
์คํธ ๊ตฌ์กฐ๋ ์ฌ๋์ด ๋์์ธํ ํ์ต ๊ณผ์ ์ด ์ผ๋ฐํ ์ฑ๋ฅ์ ํฌ๊ฒ ํฅ์ํ ์ ์๋ค๋ ๊ฒ์ ๋ณด์ธ๋ค. ๊ทธ๋ฆฌ๊ณ ์ ์๋ ๊ฒฝ๊ณ์ ํ์ต ๊ธฐ๋ฒ์ผ๋ก CNN์ ์ฌ์ฉํ ์์ ๋ถํ ์ ์ฑ๋ฅ์ ํฅ์ํ ์ ์์์ ๋ดํฌํ๋ค. ๊ฐ ํ๊ด์ ๋ถํ ์ ์ด์ฐจ์ ์ต๋ ๊ฐ๋ ํฌ์ ๊ธฐ๋ฐ ์ด๋ฏธ์ง๋ก๋ถํฐ ํ๋๋ ํ๊ด ํ๋ณด ์ ๋ค์ ํตํด ์์ ํ๊ด๋ค์ด ์ฑ๊ณต์ ์ผ๋ก ๋ถํ ๋ ์ ์์์ ๋ณด์ธ๋ค. ๋ณธ ๋
ผ๋ฌธ์์ ์ ์๋ ์๊ณ ๋ฆฌ์ฆ์ ๊ฐ์ ํด๋ถํ์ ๋ถ์๊ณผ ์๋ํ๋ ์ปดํจํฐ ์ง๋จ ๋ณด์กฐ ์์คํ
์ ๊ตฌ์ถํ๋ ๋ฐ ๋งค์ฐ ์ค์ํ ๊ธฐ์ ์ด๋ค.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
์ด๋ก 197Docto
Automated liver tissues delineation techniques: A systematic survey on machine learning current trends and future orientations
Machine learning and computer vision techniques have grown rapidly in recent years due to their automation, suitability, and ability to generate astounding results. Hence, in this paper, we survey the key studies that are published between 2014 and 2022, showcasing the different machine learning algorithms researchers have used to segment the liver, hepatic tumors, and hepatic-vasculature structures. We divide the surveyed studies based on the tissue of interest (hepatic-parenchyma, hepatic-tumors, or hepatic-vessels), highlighting the studies that tackle more than one task simultaneously. Additionally, the machine learning algorithms are classified as either supervised or unsupervised, and they are further partitioned if the amount of work that falls under a certain scheme is significant. Moreover, different datasets and challenges found in literature and websites containing masks of the aforementioned tissues are thoroughly discussed, highlighting the organizers' original contributions and those of other researchers. Also, the metrics used excessively in the literature are mentioned in our review, stressing their relevance to the task at hand. Finally, critical challenges and future directions are emphasized for innovative researchers to tackle, exposing gaps that need addressing, such as the scarcity of many studies on the vessels' segmentation challenge and why their absence needs to be dealt with sooner than later. 2022 The Author(s)This publication was made possible by an Award [GSRA6-2-0521-19034] from Qatar National Research Fund (a member of Qatar Foundation). The contents herein are solely the responsibility of the authors. Open Access funding provided by the Qatar National LibraryScopu
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