U-Net and its variants for medical image segmentation: theory and applications

U-net is an image segmentation technique developed primarily for medical image analysis that can precisely segment images using a scarce amount of training data. These traits provide U-net with a very high utility within the medical imaging community and have resulted in extensive adoption of U-net as the primary tool for segmentation tasks in medical imaging. The success of U-net is evident in its widespread use in all major image modalities from CT scans and MRI to X-rays and microscopy. Furthermore, while U-net is largely a segmentation tool, there have been instances of the use of U-net in other applications. As the potential of U-net is still increasing, in this review we look at the various developments that have been made in the U-net architecture and provide observations on recent trends. We examine the various innovations that have been made in deep learning and discuss how these tools facilitate U-net. Furthermore, we look at image modalities and application areas where U-net has been applied.Comment: 42 pages, in IEEE Acces

복부 CT에서 간과 혈관 분할 기법

학위논문(박사)--서울대학교 대학원 :공과대학 컴퓨터공학부,2020. 2. 신영길.복부 전산화 단층 촬영 (CT) 영상에서 정확한 간 및 혈관 분할은 체적 측정, 치료 계획 수립 및 추가적인 증강 현실 기반 수술 가이드와 같은 컴퓨터 진단 보조 시스템을 구축하는데 필수적인 요소이다. 최근 들어 컨볼루셔널 인공 신경망 (CNN) 형태의 딥 러닝이 많이 적용되면서 의료 영상 분할의 성능이 향상되고 있지만, 실제 임상에 적용할 수 있는 높은 일반화 성능을 제공하기는 여전히 어렵다. 또한 물체의 경계는 전통적으로 영상 분할에서 매우 중요한 요소로 이용되었지만, CT 영상에서 간의 불분명한 경계를 추출하기가 어렵기 때문에 현대 CNN에서는 이를 사용하지 않고 있다. 간 혈관 분할 작업의 경우, 복잡한 혈관 영상으로부터 학습 데이터를 만들기 어렵기 때문에 딥 러닝을 적용하기가 어렵다. 또한 얇은 혈관 부분의 영상 밝기 대비가 약하여 원본 영상에서 식별하기가 매우 어렵다. 본 논문에서는 위 언급한 문제들을 해결하기 위해 일반화 성능이 향상된 CNN과 얇은 혈관을 포함하는 복잡한 간 혈관을 정확하게 분할하는 알고리즘을 제안한다. 간 분할 작업에서 우수한 일반화 성능을 갖는 CNN을 구축하기 위해, 내부적으로 간 모양을 추정하는 부분이 포함된 자동 컨텍스트 알고리즘을 제안한다. 또한, CNN을 사용한 학습에 경계선의 개념이 새롭게 제안된다. 모호한 경계부가 포함되어 있어 전체 경계 영역을 CNN에 훈련하는 것은 매우 어렵기 때문에 반복되는 학습 과정에서 인공 신경망이 스스로 예측한 확률에서 부정확하게 추정된 부분적 경계만을 사용하여 인공 신경망을 학습한다. 실험적 결과를 통해 제안된 CNN이 다른 최신 기법들보다 정확도가 우수하다는 것을 보인다. 또한, 제안된 CNN의 일반화 성능을 검증하기 위해 다양한 실험을 수행한다. 간 혈관 분할에서는 간 내부의 관심 영역을 지정하기 위해 앞서 획득한 간 영역을 활용한다. 정확한 간 혈관 분할을 위해 혈관 후보 점들을 추출하여 사용하는 알고리즘을 제안한다. 확실한 후보 점들을 얻기 위해, 삼차원 영상의 차원을 먼저 최대 강도 투영 기법을 통해 이차원으로 낮춘다. 이차원 영상에서는 복잡한 혈관의 구조가 보다 단순화될 수 있다. 이어서, 이차원 영상에서 혈관 분할을 수행하고 혈관 픽셀들은 원래의 삼차원 공간상으로 역 투영된다. 마지막으로, 전체 혈관의 분할을 위해 원본 영상과 혈관 후보 점들을 모두 사용하는 새로운 레벨 셋 기반 알고리즘을 제안한다. 제안된 알고리즘은 복잡한 구조가 단순화되고 얇은 혈관이 더 잘 보이는 이차원 영상에서 얻은 후보 점들을 사용하기 때문에 얇은 혈관 분할에서 높은 정확도를 보인다. 실험적 결과에 의하면 제안된 알고리즘은 잘못된 영역의 추출 없이 다른 레벨 셋 기반 알고리즘들보다 우수한 성능을 보인다. 제안된 알고리즘은 간과 혈관을 분할하는 새로운 방법을 제시한다. 제안된 자동 컨텍스트 구조는 사람이 디자인한 학습 과정이 일반화 성능을 크게 향상할 수 있다는 것을 보인다. 그리고 제안된 경계선 학습 기법으로 CNN을 사용한 영상 분할의 성능을 향상할 수 있음을 내포한다. 간 혈관의 분할은 이차원 최대 강도 투영 기반 이미지로부터 획득된 혈관 후보 점들을 통해 얇은 혈관들이 성공적으로 분할될 수 있음을 보인다. 본 논문에서 제안된 알고리즘은 간의 해부학적 분석과 자동화된 컴퓨터 진단 보조 시스템을 구축하는 데 매우 중요한 기술이다.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 초록 197Docto

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

Deep learning for image-based liver analysis — A comprehensive review focusing on malignant lesions

Deep learning-based methods, in particular, convolutional neural networks and fully convolutional networks are now widely used in the medical image analysis domain. The scope of this review focuses on the analysis using deep learning of focal liver lesions, with a special interest in hepatocellular carcinoma and metastatic cancer; and structures like the parenchyma or the vascular system. Here, we address several neural network architectures used for analyzing the anatomical structures and lesions in the liver from various imaging modalities such as computed tomography, magnetic resonance imaging and ultrasound. Image analysis tasks like segmentation, object detection and classification for the liver, liver vessels and liver lesions are discussed. Based on the qualitative search, 91 papers were filtered out for the survey, including journal publications and conference proceedings. The papers reviewed in this work are grouped into eight categories based on the methodologies used. By comparing the evaluation metrics, hybrid models performed better for both the liver and the lesion segmentation tasks, ensemble classifiers performed better for the vessel segmentation tasks and combined approach performed better for both the lesion classification and detection tasks. The performance was measured based on the Dice score for the segmentation, and accuracy for the classification and detection tasks, which are the most commonly used metrics.publishedVersio