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Han T, Ai D, Fan J, Song H, Xiao D, Wang Y, Yang J. Cross-Anatomy Transfer Learning via Shape-Aware Adaptive Fine-Tuning for 3D Vessel Segmentation. IEEE J Biomed Health Inform 2024; 28:6064-6077. [PMID: 38954568 DOI: 10.1109/jbhi.2024.3422177] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 07/04/2024]
Abstract
Deep learning methods have recently achieved remarkable performance in vessel segmentation applications, yet require numerous labor-intensive labeled data. To alleviate the requirement of manual annotation, transfer learning methods can potentially be used to acquire the related knowledge of tubular structures from public large-scale labeled vessel datasets for target vessel segmentation in other anatomic sites of the human body. However, the cross-anatomy domain shift is a challenging task due to the formidable discrepancy among various vessel structures in different anatomies, resulting in the limited performance of transfer learning. Therefore, we propose a cross-anatomy transfer learning framework for 3D vessel segmentation, which first generates a pre-trained model on a public hepatic vessel dataset and then adaptively fine-tunes our target segmentation network initialized from the model for segmentation of other anatomic vessels. In the framework, the adaptive fine-tuning strategy is presented to dynamically decide on the frozen or fine-tuned filters of the target network for each input sample with a proxy network. Moreover, we develop a Gaussian-based signed distance map that explicitly encodes vessel-specific shape context. The prediction of the map is added as an auxiliary task in the segmentation network to capture geometry-aware knowledge in the fine-tuning. We demonstrate the effectiveness of our method through extensive experiments on two small-scale datasets of coronary artery and brain vessel. The results indicate the proposed method effectively overcomes the discrepancy of cross-anatomy domain shift to achieve accurate vessel segmentation for these two datasets.
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Dalvit Carvalho da Silva R, Soltanzadeh R, Figley CR. Automated Coronary Artery Tracking with a Voronoi-Based 3D Centerline Extraction Algorithm. J Imaging 2023; 9:268. [PMID: 38132686 PMCID: PMC10743762 DOI: 10.3390/jimaging9120268] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/10/2023] [Revised: 11/22/2023] [Accepted: 11/24/2023] [Indexed: 12/23/2023] Open
Abstract
Coronary artery disease is one of the leading causes of death worldwide, and medical imaging methods such as coronary artery computed tomography are vitally important in its detection. More recently, various computational approaches have been proposed to automatically extract important artery coronary features (e.g., vessel centerlines, cross-sectional areas along vessel branches, etc.) that may ultimately be able to assist with more accurate and timely diagnoses. The current study therefore validated and benchmarked a recently developed automated 3D centerline extraction method for coronary artery centerline tracking using synthetically segmented coronary artery models based on the widely used Rotterdam Coronary Artery Algorithm Evaluation Framework (RCAAEF) training dataset. Based on standard accuracy metrics and the ground truth centerlines of all 32 coronary vessel branches in the RCAAEF training dataset, this 3D divide and conquer Voronoi diagram method performed exceptionally well, achieving an average overlap accuracy (OV) of 99.97%, overlap until first error (OF) of 100%, overlap of the clinically relevant portion of the vessel (OT) of 99.98%, and an average error distance inside the vessels (AI) of only 0.13 mm. Accuracy was also found to be exceptionally for all four coronary artery sub-types, with average OV values of 99.99% for right coronary arteries, 100% for left anterior descending arteries, 99.96% for left circumflex arteries, and 100% for large side-branch vessels. These results validate that the proposed method can be employed to quickly, accurately, and automatically extract 3D centerlines from segmented coronary arteries, and indicate that it is likely worthy of further exploration given the importance of this topic.
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Affiliation(s)
- Rodrigo Dalvit Carvalho da Silva
- Department of Radiology, Rady Faculty of Health Sciences, University of Manitoba, Winnipeg, MB R3T 2N2, Canada;
- Division of Diagnostic Imaging, Health Sciences Centre, Shared Health Manitoba, Winnipeg, MB R3T 2N2, Canada
| | - Ramin Soltanzadeh
- Department of Radiology, Rady Faculty of Health Sciences, University of Manitoba, Winnipeg, MB R3T 2N2, Canada;
- Division of Diagnostic Imaging, Health Sciences Centre, Shared Health Manitoba, Winnipeg, MB R3T 2N2, Canada
- Biomedical Engineering Program, Price Faculty of Engineering, University of Manitoba, Winnipeg, MB R3T 5V6, Canada
| | - Chase R. Figley
- Department of Radiology, Rady Faculty of Health Sciences, University of Manitoba, Winnipeg, MB R3T 2N2, Canada;
- Division of Diagnostic Imaging, Health Sciences Centre, Shared Health Manitoba, Winnipeg, MB R3T 2N2, Canada
- Biomedical Engineering Program, Price Faculty of Engineering, University of Manitoba, Winnipeg, MB R3T 5V6, Canada
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Nader R, Bourcier R, Autrusseau F. Using deep learning for an automatic detection and classification of the vascular bifurcations along the Circle of Willis. Med Image Anal 2023; 89:102919. [PMID: 37619447 DOI: 10.1016/j.media.2023.102919] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/31/2023] [Revised: 06/01/2023] [Accepted: 07/31/2023] [Indexed: 08/26/2023]
Abstract
Most of the intracranial aneurysms (ICA) occur on a specific portion of the cerebral vascular tree named the Circle of Willis (CoW). More particularly, they mainly arise onto fifteen of the major arterial bifurcations constituting this circular structure. Hence, for an efficient and timely diagnosis it is critical to develop some methods being able to accurately recognize each Bifurcation of Interest (BoI). Indeed, an automatic extraction of the bifurcations presenting the higher risk of developing an ICA would offer the neuroradiologists a quick glance at the most alarming areas. Due to the recent efforts on Artificial Intelligence, Deep Learning turned out to be the best performing technology for many pattern recognition tasks. Moreover, various methods have been particularly designed for medical image analysis purposes. This study intends to assist the neuroradiologists to promptly locate any bifurcation presenting a high risk of ICA occurrence. It can be seen as a Computer Aided Diagnosis scheme, where the Artificial Intelligence facilitates the access to the regions of interest within the MRI. In this work, we propose a method for a fully automatic detection and recognition of the bifurcations of interest forming the Circle of Willis. Several neural networks architectures have been tested, and we thoroughly evaluate the bifurcation recognition rate.
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Affiliation(s)
- Rafic Nader
- Nantes Université, CHU Nantes, CNRS, INSERM, l'institut du thorax, F-44000 Nantes, France
| | - Romain Bourcier
- Nantes Université, CHU Nantes, CNRS, INSERM, l'institut du thorax, F-44000 Nantes, France
| | - Florent Autrusseau
- Nantes Université, CHU Nantes, CNRS, INSERM, l'institut du thorax, F-44000 Nantes, France; Nantes Université, Polytech'Nantes, LTeN, U-6607, Rue Ch. Pauc, 44306, Nantes, France.
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Zeng A, Wu C, Lin G, Xie W, Hong J, Huang M, Zhuang J, Bi S, Pan D, Ullah N, Khan KN, Wang T, Shi Y, Li X, Xu X. ImageCAS: A large-scale dataset and benchmark for coronary artery segmentation based on computed tomography angiography images. Comput Med Imaging Graph 2023; 109:102287. [PMID: 37634975 DOI: 10.1016/j.compmedimag.2023.102287] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/15/2023] [Revised: 05/03/2023] [Accepted: 08/03/2023] [Indexed: 08/29/2023]
Abstract
Cardiovascular disease (CVD) accounts for about half of non-communicable diseases. Vessel stenosis in the coronary artery is considered to be the major risk of CVD. Computed tomography angiography (CTA) is one of the widely used noninvasive imaging modalities in coronary artery diagnosis due to its superior image resolution. Clinically, segmentation of coronary arteries is essential for the diagnosis and quantification of coronary artery disease. Recently, a variety of works have been proposed to address this problem. However, on one hand, most works rely on in-house datasets, and only a few works published their datasets to the public which only contain tens of images. On the other hand, their source code have not been published, and most follow-up works have not made comparison with existing works, which makes it difficult to judge the effectiveness of the methods and hinders the further exploration of this challenging yet critical problem in the community. In this paper, we propose a large-scale dataset for coronary artery segmentation on CTA images. In addition, we have implemented a benchmark in which we have tried our best to implement several typical existing methods. Furthermore, we propose a strong baseline method which combines multi-scale patch fusion and two-stage processing to extract the details of vessels. Comprehensive experiments show that the proposed method achieves better performance than existing works on the proposed large-scale dataset. The benchmark and the dataset are published at https://github.com/XiaoweiXu/ImageCAS-A-Large-Scale-Dataset-and-Benchmark-for-Coronary-Artery-Segmentation-based-on-CT.
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Affiliation(s)
- An Zeng
- School of Computer Science, Guangdong University of Technology, Guangzhou, China
| | - Chunbiao Wu
- School of Computer Science, Guangdong University of Technology, Guangzhou, China
| | - Guisen Lin
- Department of Radiology, Shenzhen Children's Hospital, Shenzhen, China
| | - Wen Xie
- Guangdong Provincial Key Laboratory of South China Structural Heart Disease, Guangdong Provincial People's Hospital (Guangdong Academy of Medical Sciences), Southern Medical University, Guangzhou, 510080, China
| | - Jin Hong
- Guangdong Provincial Key Laboratory of South China Structural Heart Disease, Guangdong Provincial People's Hospital (Guangdong Academy of Medical Sciences), Southern Medical University, Guangzhou, 510080, China
| | - Meiping Huang
- Guangdong Provincial Key Laboratory of South China Structural Heart Disease, Guangdong Provincial People's Hospital (Guangdong Academy of Medical Sciences), Southern Medical University, Guangzhou, 510080, China
| | - Jian Zhuang
- Guangdong Provincial Key Laboratory of South China Structural Heart Disease, Guangdong Provincial People's Hospital (Guangdong Academy of Medical Sciences), Southern Medical University, Guangzhou, 510080, China
| | - Shanshan Bi
- Department of Computer Science and Engineering, Missouri University of Science and Technology, Rolla, MO, United States
| | - Dan Pan
- Department of Computer Science, Guangdong Polytechnic Normal University, Guangzhou, China
| | - Najeeb Ullah
- Department of Computer Science, University of Engineering and Technology, Mardan, KP, Pakistan
| | - Kaleem Nawaz Khan
- Department of Computer Science, University of Engineering and Technology, Mardan, KP, Pakistan
| | - Tianchen Wang
- Department of Computer Science and Engineering, University of Notre Dame, Indiana, United States
| | - Yiyu Shi
- Department of Computer Science and Engineering, University of Notre Dame, Indiana, United States
| | - Xiaomeng Li
- Department of Electronic and Computer Engineering, The Hong Kong University of Science and Technology, Hong Kong Special Administrative Region, China
| | - Xiaowei Xu
- Guangdong Provincial Key Laboratory of South China Structural Heart Disease, Guangdong Provincial People's Hospital (Guangdong Academy of Medical Sciences), Southern Medical University, Guangzhou, 510080, China.
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Bansal A, Kaushik S, Bihonegn T, Slovák J. Automatic tractography and segmentation using finsler geometry based on higher-order tensor fields. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2023; 240:107630. [PMID: 37320943 DOI: 10.1016/j.cmpb.2023.107630] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/29/2022] [Revised: 04/08/2023] [Accepted: 05/28/2023] [Indexed: 06/17/2023]
Abstract
BACKGROUND AND OBJECTIVE We focus on three-dimensional higher-order tensorial (HOT) images using Finsler geometry. In biomedical image analysis, these images are widely used, and they are based on the diffusion profiles inside the voxels. The diffusion information is stored in the so-called diffusion tensor D. Our objective is to present new methods revealing the architecture of neural fibers in presence of crossings and high curvatures. After tracking the fibers, we achieve direct 3D image segmentation to analyse the brain's white matter structures. METHODS To deal with the construction of the underlying fibers, the inverse of the second-order diffusion tensor D, understood as the metric tensor D-1, is commonly used in DTI modality. For crossing and highly curved fibers, higher order tensors are more relevant, but it is challenging to find an analogue of such an inverse in the HOT case. We employ an innovative approach to metrics based on higher order tensors to track the fibers properly. We propose to feed the tracked fibers as the internal initial contours in an efficient version of 3D segmentation. RESULTS We propose a brand-new approach to the inversion of a diffusion HOT, and an effective way of fiber tracking in the Finsler setting, based on innovative classification of the individual voxels. Thus, we can handle complex structures with high curvatures and crossings, even in the presence of noise. Based on our novel tractography approach, we also introduce a new segmentation method. We feed the detected fibers as the initial position of the contour surfaces to segment the image using a relevant active contour method (i.e., initiating the segmentation from inside the structures). CONCLUSIONS This is a pilot work, enhancing methods for fiber tracking and segmentation. The implemented algorithms were successfully tested on both synthetic and real data. The new features make our algorithms robust and fast, and they allow distinguishing individual objects in complex structures, even under noise.
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Affiliation(s)
- Avinash Bansal
- Department of Mathematics and Statistics, Masaryk University, Faculty of Science, Kotlářská 2, Brno 611 37, Czech Republic
| | - Sumit Kaushik
- Department of Radiology and Nuclear Medicine, St. Olav's University Hospital, Trondheim, Norway; Department of Circulation and Medical Imaging, NTNU - Norwegian University of Science and Technology, Trondheim, Norway
| | - Temesgen Bihonegn
- Department of Mathematics and Statistics, Masaryk University, Faculty of Science, Kotlářská 2, Brno 611 37, Czech Republic
| | - Jan Slovák
- Department of Mathematics and Statistics, Masaryk University, Faculty of Science, Kotlářská 2, Brno 611 37, Czech Republic.
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Wu M, Qian Y, Liao X, Wang Q, Heng PA. Hepatic vessel segmentation based on 3D swin-transformer with inductive biased multi-head self-attention. BMC Med Imaging 2023; 23:91. [PMID: 37422639 PMCID: PMC10329304 DOI: 10.1186/s12880-023-01045-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/27/2022] [Accepted: 06/05/2023] [Indexed: 07/10/2023] Open
Abstract
PURPOSE Segmentation of liver vessels from CT images is indispensable prior to surgical planning and aroused a broad range of interest in the medical image analysis community. Due to the complex structure and low-contrast background, automatic liver vessel segmentation remains particularly challenging. Most of the related researches adopt FCN, U-net, and V-net variants as a backbone. However, these methods mainly focus on capturing multi-scale local features which may produce misclassified voxels due to the convolutional operator's limited locality reception field. METHODS We propose a robust end-to-end vessel segmentation network called Inductive BIased Multi-Head Attention Vessel Net(IBIMHAV-Net) by expanding swin transformer to 3D and employing an effective combination of convolution and self-attention. In practice, we introduce voxel-wise embedding rather than patch-wise embedding to locate precise liver vessel voxels and adopt multi-scale convolutional operators to gain local spatial information. On the other hand, we propose the inductive biased multi-head self-attention which learns inductively biased relative positional embedding from initialized absolute position embedding. Based on this, we can gain more reliable queries and key matrices. RESULTS We conducted experiments on the 3DIRCADb dataset. The average dice and sensitivity of the four tested cases were 74.8[Formula: see text] and 77.5[Formula: see text], which exceed the results of existing deep learning methods and improved graph cuts method. The Branches Detected(BD)/Tree-length Detected(TD) indexes also proved the global/local feature capture ability better than other methods. CONCLUSION The proposed model IBIMHAV-Net provides an automatic, accurate 3D liver vessel segmentation with an interleaved architecture that better utilizes both global and local spatial features in CT volumes. It can be further extended for other clinical data.
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Affiliation(s)
- Mian Wu
- Guangdong Provincial Key Laboratory of Computer Vision and Virtual Reality Technology, Shenzhen Institute of Advanced Technology, Chinese Academy of Science, Shenzhen, China
| | - Yinling Qian
- Guangdong Provincial Key Laboratory of Computer Vision and Virtual Reality Technology, Shenzhen Institute of Advanced Technology, Chinese Academy of Science, Shenzhen, China
| | - Xiangyun Liao
- Guangdong Provincial Key Laboratory of Computer Vision and Virtual Reality Technology, Shenzhen Institute of Advanced Technology, Chinese Academy of Science, Shenzhen, China.
| | - Qiong Wang
- Guangdong Provincial Key Laboratory of Computer Vision and Virtual Reality Technology, Shenzhen Institute of Advanced Technology, Chinese Academy of Science, Shenzhen, China
| | - Pheng-Ann Heng
- Guangdong Provincial Key Laboratory of Computer Vision and Virtual Reality Technology, Shenzhen Institute of Advanced Technology, Chinese Academy of Science, Shenzhen, China
- The Chinese University of Hong Kong, Hong Kong SAR, China
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7
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Bertanha M, Mellucci Filho PL, Genka CA, de Camargo PAB, Grillo VTRDS, Sertório ND, Rodrigues LDS, Sobreira ML, Lourenção PLTDA. Quantitative analysis validation for sclerotherapy treatment of lower limb telangiectasias. J Vasc Surg Venous Lymphat Disord 2023; 11:708-715. [PMID: 37030450 DOI: 10.1016/j.jvsv.2023.03.010] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/07/2023] [Revised: 02/28/2023] [Accepted: 03/07/2023] [Indexed: 04/10/2023]
Abstract
BACKGROUND The evaluation of sclerotherapy efficacy for lower limb telangiectasias, which is the standard treatment for such condition, is commonly assisted by scores based on before and after pictures. This method is marked by its subjectivity, which impairs the precision of studies on the subject, making it unfeasible to evaluate and compare different interventions. We hypothesize that a quantitative method for evaluating the effectiveness of sclerotherapy for lower limb telangiectasias may present more reproducible results. Reliable measurement methods and new technologies may become part of the clinical practice in the near future. METHODS Before and after treatment photographs were analyzed using a quantitative method and compared with a validated qualitative method based on improvement scores. Reliability analysis of the methods was performed, applying the intraclass correlation coefficient (ICC) and kappa coefficient with quadratic weights (Fleiss Cohen), for analysis of inter-examiner and intra-examiner agreement in both evaluation methods. Convergent validity was evaluated by applying the Spearman test. To assess the applicability of the quantitative scale, the Mann-Whitney test was used. RESULTS A better agreement between examiners is shown for the quantitative scale, with a mean kappa of .3986 (.251-.511) for qualitative analysis and a mean kappa of .788 (.655-.918) for quantitative analysis (P < .001 for all examiners). Convergent validity was achieved by correlation coefficients of .572 to .905 (P < .001). The quantitative scale results obtained between the specialists with different degrees of experience did not show statistical difference (seniors: 0.71 [-0.48/1.00] × juniors: 0.73 [-0.34/1.00]; P = .221). CONCLUSIONS Convergent validity between both analyses has been achieved, but quantitative analysis has been shown to be more reliable and can be applied by professionals of any degree of experience. The validation of quantitative analysis is a major milestone for the development of new technology and automated, reliable, applications.
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Affiliation(s)
- Matheus Bertanha
- Department of Surgery and Orthopedics, Sao Paulo State University (UNESP), Botucatu, Sao Paulo, Brazil.
| | | | - Caroline Araujo Genka
- Department of Surgery and Orthopedics, Sao Paulo State University (UNESP), Botucatu, Sao Paulo, Brazil
| | | | | | - Nathalia Dias Sertório
- Department of Surgery and Orthopedics, Sao Paulo State University (UNESP), Botucatu, Sao Paulo, Brazil
| | - Lenize da Silva Rodrigues
- Department of Surgery and Orthopedics, Sao Paulo State University (UNESP), Botucatu, Sao Paulo, Brazil
| | - Marcone Lima Sobreira
- Department of Surgery and Orthopedics, Sao Paulo State University (UNESP), Botucatu, Sao Paulo, Brazil
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Sun Q, Yang J, Zhao S, Chen C, Hou Y, Yuan Y, Ma S, Huang Y. LIVE-Net: Comprehensive 3D vessel extraction framework in CT angiography. Comput Biol Med 2023; 159:106886. [PMID: 37062255 DOI: 10.1016/j.compbiomed.2023.106886] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/15/2022] [Revised: 03/04/2023] [Accepted: 04/01/2023] [Indexed: 04/18/2023]
Abstract
The extraction of vessels from computed tomography angiography (CTA) is significant in diagnosing and evaluating vascular diseases. However, due to the anatomical complexity, wide intensity distribution, and small volume proportion of vessels, vessel extraction is laborious and time-consuming, and it is easy to lead to error-prone diagnostic results in clinical practice. This study proposes a novel comprehensive vessel extraction framework, called the Local Iterative-based Vessel Extraction Network (LIVE-Net), to achieve 3D vessel segmentation while tracking vessel centerlines. LIVE-Net contains dual dataflow pathways that work alternately: an iterative tracking network and a local segmentation network. The former can generate the fine-grain direction and radius prediction of a vascular patch by using the attention-embedded atrous pyramid network (aAPN), and the latter can achieve 3D vascular lumen segmentation by constructing the multi-order self-attention U-shape network (MOSA-UNet). LIVE-Net is trained and evaluated on two datasets: the MICCAI 2008 Coronary Artery Tracking Challenge (CAT08) dataset and head and neck CTA dataset from the clinic. Experimental results of both tracking and segmentation show that our proposed LIVE-Net exhibits superior performance compared with other state-of-the-art (SOTA) networks. In the CAT08 dataset, the tracked centerlines have an average overlap of 95.2%, overlap until first error of 91.2%, overlap with the clinically relevant vessels of 98.3%, and error distance inside of 0.21 mm. The corresponding tracking overlap metrics in the head and neck CTA dataset are 96.7%, 91.0%, and 99.8%, respectively. In addition, the results of the consistent experiment also show strong clinical correspondence. For the segmentation of bilateral carotid and vertebral arteries, our method can not only achieve better accuracy with an average dice similarity coefficient (DSC) of 90.03%, Intersection over Union (IoU) of 81.97%, and 95% Hausdorff distance (95%HD) of 3.42 mm , but higher efficiency with an average time of 67.25 s , even three times faster compared to some methods applied in full field view. Both the tracking and segmentation results prove the potential clinical utility of our network.
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Affiliation(s)
- Qi Sun
- Key Laboratory of Intelligent Computing in Medical Image, Ministry of Education, Northeastern University, Shenyang, Liaoning, China; School of Computer Science and Engineering, Northeastern University, Shenyang, Liaoning, China
| | - Jinzhu Yang
- Key Laboratory of Intelligent Computing in Medical Image, Ministry of Education, Northeastern University, Shenyang, Liaoning, China; School of Computer Science and Engineering, Northeastern University, Shenyang, Liaoning, China.
| | - Sizhe Zhao
- Key Laboratory of Intelligent Computing in Medical Image, Ministry of Education, Northeastern University, Shenyang, Liaoning, China; School of Computer Science and Engineering, Northeastern University, Shenyang, Liaoning, China
| | - Chen Chen
- Northeastern University, Shenyang, Liaoning, China
| | - Yang Hou
- Department of Radiology, ShengJing Hospital of China Medical University, Shenyang, Liaoning, China
| | - Yuliang Yuan
- Key Laboratory of Intelligent Computing in Medical Image, Ministry of Education, Northeastern University, Shenyang, Liaoning, China; School of Computer Science and Engineering, Northeastern University, Shenyang, Liaoning, China
| | - Shuang Ma
- Key Laboratory of Intelligent Computing in Medical Image, Ministry of Education, Northeastern University, Shenyang, Liaoning, China; School of Computer Science and Engineering, Northeastern University, Shenyang, Liaoning, China
| | - Yan Huang
- Key Laboratory of Intelligent Computing in Medical Image, Ministry of Education, Northeastern University, Shenyang, Liaoning, China; School of Computer Science and Engineering, Northeastern University, Shenyang, Liaoning, China
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Li H, Tang Z, Nan Y, Yang G. Human treelike tubular structure segmentation: A comprehensive review and future perspectives. Comput Biol Med 2022; 151:106241. [PMID: 36379190 DOI: 10.1016/j.compbiomed.2022.106241] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/12/2022] [Revised: 09/16/2022] [Accepted: 10/22/2022] [Indexed: 12/27/2022]
Abstract
Various structures in human physiology follow a treelike morphology, which often expresses complexity at very fine scales. Examples of such structures are intrathoracic airways, retinal blood vessels, and hepatic blood vessels. Large collections of 2D and 3D images have been made available by medical imaging modalities such as magnetic resonance imaging (MRI), computed tomography (CT), Optical coherence tomography (OCT) and ultrasound in which the spatial arrangement can be observed. Segmentation of these structures in medical imaging is of great importance since the analysis of the structure provides insights into disease diagnosis, treatment planning, and prognosis. Manually labelling extensive data by radiologists is often time-consuming and error-prone. As a result, automated or semi-automated computational models have become a popular research field of medical imaging in the past two decades, and many have been developed to date. In this survey, we aim to provide a comprehensive review of currently publicly available datasets, segmentation algorithms, and evaluation metrics. In addition, current challenges and future research directions are discussed.
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Affiliation(s)
- Hao Li
- National Heart and Lung Institute, Faculty of Medicine, Imperial College London, London, United Kingdom; Department of Bioengineering, Faculty of Engineering, Imperial College London, London, United Kingdom
| | - Zeyu Tang
- National Heart and Lung Institute, Faculty of Medicine, Imperial College London, London, United Kingdom; Department of Bioengineering, Faculty of Engineering, Imperial College London, London, United Kingdom
| | - Yang Nan
- National Heart and Lung Institute, Faculty of Medicine, Imperial College London, London, United Kingdom
| | - Guang Yang
- National Heart and Lung Institute, Faculty of Medicine, Imperial College London, London, United Kingdom; Royal Brompton Hospital, London, United Kingdom.
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Semi-supervised region-connectivity-based cerebrovascular segmentation for time-of-flight magnetic resonance angiography image. Comput Biol Med 2022; 149:105972. [DOI: 10.1016/j.compbiomed.2022.105972] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/22/2022] [Revised: 07/24/2022] [Accepted: 08/13/2022] [Indexed: 11/18/2022]
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Abdominal vessel segmentation using vessel model embedded fuzzy C-means and similarity from CT angiography. Med Biol Eng Comput 2022; 60:3325-3340. [DOI: 10.1007/s11517-022-02644-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/18/2021] [Accepted: 08/09/2022] [Indexed: 11/25/2022]
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12
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Generative adversarial network based cerebrovascular segmentation for time-of-flight magnetic resonance angiography image. Neurocomputing 2022. [DOI: 10.1016/j.neucom.2021.11.075] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/28/2023]
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13
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Li X, Bala R, Monga V. Robust Deep 3D Blood Vessel Segmentation Using Structural Priors. IEEE TRANSACTIONS ON IMAGE PROCESSING : A PUBLICATION OF THE IEEE SIGNAL PROCESSING SOCIETY 2022; 31:1271-1284. [PMID: 34990361 DOI: 10.1109/tip.2021.3139241] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/14/2023]
Abstract
Deep learning has enabled significant improvements in the accuracy of 3D blood vessel segmentation. Open challenges remain in scenarios where labeled 3D segmentation maps for training are severely limited, as is often the case in practice, and in ensuring robustness to noise. Inspired by the observation that 3D vessel structures project onto 2D image slices with informative and unique edge profiles, we propose a novel deep 3D vessel segmentation network guided by edge profiles. Our network architecture comprises a shared encoder and two decoders that learn segmentation maps and edge profiles jointly. 3D context is mined in both the segmentation and edge prediction branches by employing bidirectional convolutional long-short term memory (BCLSTM) modules. 3D features from the two branches are concatenated to facilitate learning of the segmentation map. As a key contribution, we introduce new regularization terms that: a) capture the local homogeneity of 3D blood vessel volumes in the presence of biomarkers; and b) ensure performance robustness to domain-specific noise by suppressing false positive responses. Experiments on benchmark datasets with ground truth labels reveal that the proposed approach outperforms state-of-the-art techniques on standard measures such as DICE overlap and mean Intersection-over-Union. The performance gains of our method are even more pronounced when training is limited. Furthermore, the computational cost of our network inference is among the lowest compared with state-of-the-art.
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Liu L, Chen D, Shu M, Li B, Shu H, Paques M, Cohen LD. Trajectory Grouping With Curvature Regularization for Tubular Structure Tracking. IEEE TRANSACTIONS ON IMAGE PROCESSING : A PUBLICATION OF THE IEEE SIGNAL PROCESSING SOCIETY 2021; 31:405-418. [PMID: 34874858 DOI: 10.1109/tip.2021.3131940] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/13/2023]
Abstract
Tubular structure tracking is a crucial task in the fields of computer vision and medical image analysis. The minimal paths-based approaches have exhibited their strong ability in tracing tubular structures, by which a tubular structure can be naturally modeled as a minimal geodesic path computed with a suitable geodesic metric. However, existing minimal paths-based tracing approaches still suffer from difficulties such as the shortcuts and short branches combination problems, especially when dealing with the images involving complicated tubular tree structures or background. In this paper, we introduce a new minimal paths-based model for minimally interactive tubular structure centerline extraction in conjunction with a perceptual grouping scheme. Basically, we take into account the prescribed tubular trajectories and curvature-penalized geodesic paths to seek suitable shortest paths. The proposed approach can benefit from the local smoothness prior on tubular structures and the global optimality of the used graph-based path searching scheme. Experimental results on both synthetic and real images prove that the proposed model indeed obtains outperformance comparing with the state-of-the-art minimal paths-based tubular structure tracing algorithms.
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15
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Yang Q, Ma B, Cui H, Ma J. AMF-NET: Attention-aware Multi-scale Fusion Network for Retinal Vessel Segmentation. ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2021; 2021:3277-3280. [PMID: 34891940 DOI: 10.1109/embc46164.2021.9630756] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/08/2022]
Abstract
Automatic retinal vessel segmentation in fundus image can assist effective and efficient diagnosis of retina disease. Microstructure estimation of capillaries is a prolonged challenging issue. To tackle this problem, we propose attention-aware multi-scale fusion network (AMF-Net). Our network is with dense convolutions to perceive microscopic capillaries. Additionally, multi-scale features are extracted and fused with adaptive weights by channel attention module to improve the segmentation performance. Finally, spatial attention is introduced by position attention modules to capture long-distance feature dependencies. The proposed model is evaluated using two public datasets including DRIVE and CHASE_DB1. Extensive experiments demonstrate that our model outperforms existing methods. Ablation study valid the effectiveness of the proposed components.
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16
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Dang VN, Galati F, Cortese R, Di Giacomo G, Marconetto V, Mathur P, Lekadir K, Lorenzi M, Prados F, Zuluaga MA. Vessel-CAPTCHA: An efficient learning framework for vessel annotation and segmentation. Med Image Anal 2021; 75:102263. [PMID: 34731770 DOI: 10.1016/j.media.2021.102263] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/29/2021] [Revised: 10/04/2021] [Accepted: 10/06/2021] [Indexed: 12/22/2022]
Abstract
Deep learning techniques for 3D brain vessel image segmentation have not been as successful as in the segmentation of other organs and tissues. This can be explained by two factors. First, deep learning techniques tend to show poor performances at the segmentation of relatively small objects compared to the size of the full image. Second, due to the complexity of vascular trees and the small size of vessels, it is challenging to obtain the amount of annotated training data typically needed by deep learning methods. To address these problems, we propose a novel annotation-efficient deep learning vessel segmentation framework. The framework avoids pixel-wise annotations, only requiring weak patch-level labels to discriminate between vessel and non-vessel 2D patches in the training set, in a setup similar to the CAPTCHAs used to differentiate humans from bots in web applications. The user-provided weak annotations are used for two tasks: (1) to synthesize pixel-wise pseudo-labels for vessels and background in each patch, which are used to train a segmentation network, and (2) to train a classifier network. The classifier network allows to generate additional weak patch labels, further reducing the annotation burden, and it acts as a second opinion for poor quality images. We use this framework for the segmentation of the cerebrovascular tree in Time-of-Flight angiography (TOF) and Susceptibility-Weighted Images (SWI). The results show that the framework achieves state-of-the-art accuracy, while reducing the annotation time by ∼77% w.r.t. learning-based segmentation methods using pixel-wise labels for training.
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Affiliation(s)
- Vien Ngoc Dang
- Data Science Department, EURECOM, Sophia Antipolis, France; Artificial Intelligence in Medicine Lab, Facultat de Matemátiques I Informática, Universitat de Barcelona, Spain
| | | | - Rosa Cortese
- Queen Square MS Centre, Department of Neuroinflammation, UCL Queen Square Institute of Neurology, Faculty of Brain Sciences, University College London, UK; Department of Medicine, Surgery and Neuroscience, University of Siena, Italy
| | - Giuseppe Di Giacomo
- Data Science Department, EURECOM, Sophia Antipolis, France; Politecnico di Torino, Turin, Italy
| | - Viola Marconetto
- Data Science Department, EURECOM, Sophia Antipolis, France; Politecnico di Torino, Turin, Italy
| | - Prateek Mathur
- Data Science Department, EURECOM, Sophia Antipolis, France
| | - Karim Lekadir
- Artificial Intelligence in Medicine Lab, Facultat de Matemátiques I Informática, Universitat de Barcelona, Spain
| | - Marco Lorenzi
- Université Côte d'Azur, Inria Sophia Antipolis, Epione Research Group, Valbonne, France
| | - Ferran Prados
- Centre for Medical Image Computing, Department of Medical Physics and Bioengineering, University College London, UK; Queen Square MS Centre, Department of Neuroinflammation, UCL Queen Square Institute of Neurology, Faculty of Brain Sciences, University College London, UK; National Institute for Health Research, University College London Hospitals, Biomedical Research Centre, London, UK; e-health Center, Universitat Oberta de Catalunya, Barcelona, Spain
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17
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Yan Q, Wang B, Zhang W, Luo C, Xu W, Xu Z, Zhang Y, Shi Q, Zhang L, You Z. Attention-Guided Deep Neural Network With Multi-Scale Feature Fusion for Liver Vessel Segmentation. IEEE J Biomed Health Inform 2021; 25:2629-2642. [PMID: 33264097 DOI: 10.1109/jbhi.2020.3042069] [Citation(s) in RCA: 34] [Impact Index Per Article: 11.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/10/2022]
Abstract
Liver vessel segmentation is fast becoming a key instrument in the diagnosis and surgical planning of liver diseases. In clinical practice, liver vessels are normally manual annotated by clinicians on each slice of CT images, which is extremely laborious. Several deep learning methods exist for liver vessel segmentation, however, promoting the performance of segmentation remains a major challenge due to the large variations and complex structure of liver vessels. Previous methods mainly using existing UNet architecture, but not all features of the encoder are useful for segmentation and some even cause interferences. To overcome this problem, we propose a novel deep neural network for liver vessel segmentation, called LVSNet, which employs special designs to obtain the accurate structure of the liver vessel. Specifically, we design Attention-Guided Concatenation (AGC) module to adaptively select the useful context features from low-level features guided by high-level features. The proposed AGC module focuses on capturing rich complemented information to obtain more details. In addition, we introduce an innovative multi-scale fusion block by constructing hierarchical residual-like connections within one single residual block, which is of great importance for effectively linking the local blood vessel fragments together. Furthermore, we construct a new dataset containing 40 thin thickness cases (0.625 mm) which consist of CT volumes and annotated vessels. To evaluate the effectiveness of the method with minor vessels, we also propose an automatic stratification method to split major and minor liver vessels. Extensive experimental results demonstrate that the proposed LVSNet outperforms previous methods on liver vessel segmentation datasets. Additionally, we conduct a series of ablation studies that comprehensively support the superiority of the underlying concepts.
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18
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Han T, Ai D, An R, Fan J, Song H, Wang Y, Yang J. Ordered multi-path propagation for vessel centerline extraction. Phys Med Biol 2021; 66. [PMID: 34157702 DOI: 10.1088/1361-6560/ac0d8e] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/02/2021] [Accepted: 06/22/2021] [Indexed: 11/12/2022]
Abstract
Vessel centerline extraction from x-ray angiography images is essential for vessel structure analysis in the diagnosis of coronary artery disease. However, complete and continuous centerline extraction remains a challenging task due to image noise, poor contrast, and complexity of vessel structure. Thus, an iterative multi-path search framework for automatic vessel centerline extraction is proposed. First, the seed points of the vessel structure are detected and sorted by confidence. With the ordered seed points, multi-bifurcation centerline is searched through multi-path propagation of wavefront and accumulated voting. Finally, the centerline is further extended piecewise by wavefront propagation on the basis of keypoint detection. The latter two steps are performed alternately to obtain the final centerline result. The proposed method is qualitatively and quantitatively evaluated on 1260 synthetic images and 50 clinical angiography images. The results demonstrate that our method has a highF1score of 87.8% ± 2.7% for the angiography images and achieves accurate and continuous results of vessel centerline extraction.
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Affiliation(s)
- Tao Han
- Laboratory of Beijing Engineering Research Center of Mixed Reality and Advanced Display, School of Optics and Photonics, Beijing Institute of Technology, Beijing 100081, People's Republic of China
| | - Danni Ai
- Laboratory of Beijing Engineering Research Center of Mixed Reality and Advanced Display, School of Optics and Photonics, Beijing Institute of Technology, Beijing 100081, People's Republic of China
| | - Ruirui An
- Laboratory of Beijing Engineering Research Center of Mixed Reality and Advanced Display, School of Optics and Photonics, Beijing Institute of Technology, Beijing 100081, People's Republic of China
| | - Jingfan Fan
- Laboratory of Beijing Engineering Research Center of Mixed Reality and Advanced Display, School of Optics and Photonics, Beijing Institute of Technology, Beijing 100081, People's Republic of China
| | - Hong Song
- School of Computer Science and Technology, Beijing Institute of Technology, Beijing 100081, People's Republic of China
| | - Yining Wang
- Department of Radiology, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100730, People's Republic of China
| | - Jian Yang
- Laboratory of Beijing Engineering Research Center of Mixed Reality and Advanced Display, School of Optics and Photonics, Beijing Institute of Technology, Beijing 100081, People's Republic of China
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19
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Yang J, Fu M, Hu Y. Liver vessel segmentation based on inter-scale V-Net. MATHEMATICAL BIOSCIENCES AND ENGINEERING : MBE 2021; 18:4327-4340. [PMID: 34198439 DOI: 10.3934/mbe.2021217] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/13/2023]
Abstract
Segmentation and visualization of liver vessel is a key task in preoperative planning and computer-aided diagnosis of liver diseases. Due to the irregular structure of liver vessel, accurate liver vessel segmentation is difficult. This paper proposes a method of liver vessel segmentation based on an improved V-Net network. Firstly, a dilated convolution is introduced into the network to make the network can still enlarge the receptive field without reducing down-sampling and save detailed spatial information. Secondly, a 3D deep supervision mechanism is introduced into the network to speed up the convergence of the network and help the network learn semantic features better. Finally, inter-scale dense connections are designed in the decoder of the network to prevent the loss of high-level semantic information during the decoding process and effectively integrate multi-scale feature information. The public datasets 3Dircadb were used to perform liver vessel segmentation experiments. The average dice and sensitivity of the proposed method reached 71.6 and 75.4%, respectively, which are higher than those of the original network. The experimental results show that the improved V-Net network can automatically and accurately segment labeled or even other unlabeled liver vessels from the CT images.
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Affiliation(s)
- Jinzhu Yang
- Key Laboratory of Intelligent Computing in Medical Image, Ministry of Education Northeastern University, Shenyang 110000, China
| | - Meihan Fu
- College of Marine Electrical Engineering, Dalian Maritime University, Dalian 116000, China
| | - Ying Hu
- College of Marine Electrical Engineering, Dalian Maritime University, Dalian 116000, China
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20
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Ma Y, Hao H, Xie J, Fu H, Zhang J, Yang J, Wang Z, Liu J, Zheng Y, Zhao Y. ROSE: A Retinal OCT-Angiography Vessel Segmentation Dataset and New Model. IEEE TRANSACTIONS ON MEDICAL IMAGING 2021; 40:928-939. [PMID: 33284751 DOI: 10.1109/tmi.2020.3042802] [Citation(s) in RCA: 88] [Impact Index Per Article: 29.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/12/2023]
Abstract
Optical Coherence Tomography Angiography (OCTA) is a non-invasive imaging technique that has been increasingly used to image the retinal vasculature at capillary level resolution. However, automated segmentation of retinal vessels in OCTA has been under-studied due to various challenges such as low capillary visibility and high vessel complexity, despite its significance in understanding many vision-related diseases. In addition, there is no publicly available OCTA dataset with manually graded vessels for training and validation of segmentation algorithms. To address these issues, for the first time in the field of retinal image analysis we construct a dedicated Retinal OCTA SEgmentation dataset (ROSE), which consists of 229 OCTA images with vessel annotations at either centerline-level or pixel level. This dataset with the source code has been released for public access to assist researchers in the community in undertaking research in related topics. Secondly, we introduce a novel split-based coarse-to-fine vessel segmentation network for OCTA images (OCTA-Net), with the ability to detect thick and thin vessels separately. In the OCTA-Net, a split-based coarse segmentation module is first utilized to produce a preliminary confidence map of vessels, and a split-based refined segmentation module is then used to optimize the shape/contour of the retinal microvasculature. We perform a thorough evaluation of the state-of-the-art vessel segmentation models and our OCTA-Net on the constructed ROSE dataset. The experimental results demonstrate that our OCTA-Net yields better vessel segmentation performance in OCTA than both traditional and other deep learning methods. In addition, we provide a fractal dimension analysis on the segmented microvasculature, and the statistical analysis demonstrates significant differences between the healthy control and Alzheimer's Disease group. This consolidates that the analysis of retinal microvasculature may offer a new scheme to study various neurodegenerative diseases.
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21
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Du H, Shao K, Bao F, Zhang Y, Gao C, Wu W, Zhang C. Automated coronary artery tree segmentation in coronary CTA using a multiobjective clustering and toroidal model-guided tracking method. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2021; 199:105908. [PMID: 33373814 DOI: 10.1016/j.cmpb.2020.105908] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/25/2020] [Accepted: 12/13/2020] [Indexed: 06/12/2023]
Abstract
BACKGROUND AND OBJECTIVE Accurate coronary artery tree segmentation can now be developed to assist radiologists in detecting coronary artery disease. In clinical medicine, the noise, low contrast, and uneven intensity of medical images along with complex shapes and vessel bifurcation structures make coronary artery segmentation challenging. In this work, we propose a multiobjective clustering and toroidal model-guided tracking method that can accurately extract coronary arteries from computed tomography angiography (CTA) imagery. METHODS Utilizing integrated noise reduction, candidate region detection, geometric feature extraction, and coronary artery tracking techniques, a new segmentation framework for 3D coronary artery trees is presented. The candidate regions are extracted using a multiobjective clustering method, and the coronary arteries are tracked by a toroidal model-guided tracking method. RESULTS The qualitative and quantitative results demonstrate the effectiveness of the presented framework, which achieves better performance than the compared segmentation methods in three widely used evaluation indices: the Dice similarity coefficient (DSC), Jaccard index and Recall across the CTA data. The proposed method can accurately identify the coronary artery tree with a mean DSC of 84%, a Jaccard index of 74%, and a Recall of 93%. CONCLUSIONS The proposed segmentation framework effectively segments the coronary tree from the CTA volume, which improves the accuracy of 3D vascular tree segmentation.
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Affiliation(s)
- Hongwei Du
- School of Mathmatics, Shandong University, Jinan, Shandong 250100, China; Shandong Provincial Key Laboratory of Digital Media Technology, Jinan, Shandong 250014, China
| | - Kai Shao
- Shandong Provincial Key Laboratory of Digital Media Technology, Jinan, Shandong 250014, China; School of Computer Science and Technology, Shandong University of Finance and Economics, Jinan, Shandong 250014, China
| | - Fangxun Bao
- School of Mathmatics, Shandong University, Jinan, Shandong 250100, China.
| | - Yunfeng Zhang
- Shandong Provincial Key Laboratory of Digital Media Technology, Jinan, Shandong 250014, China; School of Computer Science and Technology, Shandong University of Finance and Economics, Jinan, Shandong 250014, China
| | - Chengyong Gao
- School of Physics, Shandong University, Jinan, Shandong 250100, China
| | - Wei Wu
- Department of Cerebrovascular Diseases, Cheeloo College of Medicine, Shandong University, Jinan, Shandong 250012, China
| | - Caiming Zhang
- Shandong Provincial Key Laboratory of Digital Media Technology, Jinan, Shandong 250014, China; School of Computer Science and Technology, Shandong University, Jinan, Shandong 250101, China
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22
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Mou L, Zhao Y, Fu H, Liu Y, Cheng J, Zheng Y, Su P, Yang J, Chen L, Frangi AF, Akiba M, Liu J. CS 2-Net: Deep learning segmentation of curvilinear structures in medical imaging. Med Image Anal 2020; 67:101874. [PMID: 33166771 DOI: 10.1016/j.media.2020.101874] [Citation(s) in RCA: 98] [Impact Index Per Article: 24.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/08/2020] [Revised: 08/26/2020] [Accepted: 10/05/2020] [Indexed: 12/20/2022]
Abstract
Automated detection of curvilinear structures, e.g., blood vessels or nerve fibres, from medical and biomedical images is a crucial early step in automatic image interpretation associated to the management of many diseases. Precise measurement of the morphological changes of these curvilinear organ structures informs clinicians for understanding the mechanism, diagnosis, and treatment of e.g. cardiovascular, kidney, eye, lung, and neurological conditions. In this work, we propose a generic and unified convolution neural network for the segmentation of curvilinear structures and illustrate in several 2D/3D medical imaging modalities. We introduce a new curvilinear structure segmentation network (CS2-Net), which includes a self-attention mechanism in the encoder and decoder to learn rich hierarchical representations of curvilinear structures. Two types of attention modules - spatial attention and channel attention - are utilized to enhance the inter-class discrimination and intra-class responsiveness, to further integrate local features with their global dependencies and normalization, adaptively. Furthermore, to facilitate the segmentation of curvilinear structures in medical images, we employ a 1×3 and a 3×1 convolutional kernel to capture boundary features. Besides, we extend the 2D attention mechanism to 3D to enhance the network's ability to aggregate depth information across different layers/slices. The proposed curvilinear structure segmentation network is thoroughly validated using both 2D and 3D images across six different imaging modalities. Experimental results across nine datasets show the proposed method generally outperforms other state-of-the-art algorithms in various metrics.
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Affiliation(s)
- Lei Mou
- Cixi Institute of Biomedical Engineering, Ningbo Institute of Materials Technology and Engineering, Chinese Academy of Sciences, Ningbo, China
| | - Yitian Zhao
- Cixi Institute of Biomedical Engineering, Ningbo Institute of Materials Technology and Engineering, Chinese Academy of Sciences, Ningbo, China.
| | - Huazhu Fu
- Inception Institute of Artificial Intelligence, Abu Dhabi, United Arab Emirates
| | - Yonghuai Liu
- Department of Computer Science, Edge Hill University, Ormskirk, UK
| | - Jun Cheng
- UBTech Research, UBTech Robotics Corp Ltd, Shenzhen, China
| | - Yalin Zheng
- Department of Eye and Vision Science, University of Liverpool, Liverpool, UK; Cixi Institute of Biomedical Engineering, Ningbo Institute of Materials Technology and Engineering, Chinese Academy of Sciences, Ningbo, China
| | - Pan Su
- Cixi Institute of Biomedical Engineering, Ningbo Institute of Materials Technology and Engineering, Chinese Academy of Sciences, Ningbo, China
| | - Jianlong Yang
- Cixi Institute of Biomedical Engineering, Ningbo Institute of Materials Technology and Engineering, Chinese Academy of Sciences, Ningbo, China
| | - Li Chen
- School of Computer Science and Technology, Wuhan University of Science and Technology, Wuhan, China
| | - Alejandro F Frangi
- Cixi Institute of Biomedical Engineering, Ningbo Institute of Materials Technology and Engineering, Chinese Academy of Sciences, Ningbo, China; Centre for Computational Imaging and Simulation Technologies in Biomedicine (CISTIB), School of Computing and School of Medicine, University of Leeds, Leeds, UK; Leeds Institute of Cardiovascular and Metabolic Medicine, School of Medicine, University of Leeds, Leeds, UK; Medical Imaging Research Centre (MIRC), University Hospital Gasthuisberg, Cardiovascular Sciences and Electrical Engineering Departments, KU Leuven, Leuven, Belgium
| | | | - Jiang Liu
- Department of Computer Science and Engineering, Southern University of Science and Technology, Shenzhen, China; Cixi Institute of Biomedical Engineering, Ningbo Institute of Materials Technology and Engineering, Chinese Academy of Sciences, Ningbo, China; Guangdong Provincial Key Laboratory of Brain-inspired Intelligent Computation, Department of Computer Science and Engineering, Southern University of Science and Technology, Shenzhen, China.
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23
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Axis-guided patch based accurate segmentation for pathological vessels using adaptive weight sparse representation. Biomed Signal Process Control 2020. [DOI: 10.1016/j.bspc.2019.101817] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/23/2022]
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24
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Azar D, Torres WM, Davis LA, Shaw T, Eberth JF, Kolachalama VB, Lessner SM, Shazly T. Geometric determinants of local hemodynamics in severe carotid artery stenosis. Comput Biol Med 2019; 114:103436. [PMID: 31521900 DOI: 10.1016/j.compbiomed.2019.103436] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/05/2019] [Revised: 09/03/2019] [Accepted: 09/04/2019] [Indexed: 01/30/2023]
Abstract
In cases of severe carotid artery stenosis (CAS), carotid endarterectomy (CEA) is performed to recover lumen patency and alleviate stroke risk. Under current guidelines, the decision to surgically intervene relies primarily on the percent loss of native arterial lumen diameter within the stenotic region (i.e. the degree of stenosis). An underlying premise is that the degree of stenosis modulates flow-induced wall shear stress elevations at the lesion site, and thus indicates plaque rupture potential and stroke risk. Here, we conduct a retrospective study on pre-CEA computed tomography angiography (CTA) images from 50 patients with severe internal CAS (>60% stenosis) to better understand the influence of plaque and local vessel geometry on local hemodynamics, with geometrical descriptors that extend beyond the degree of stenosis. We first processed CTA images to define a set of multipoint geometric metrics characterizing the stenosed region, and next performed computational fluid dynamics simulations to quantify local wall shear stress and associated hemodynamic metrics. Correlation and regression analyses were used to relate obtained geometric and hemodynamic metrics, with inclusion of patient sub-classification based on the degree of stenosis. Our results suggest that in the context of severe CAS, prediction of shear stress-based metrics can be enhanced by consideration of readily available, multipoint geometric metrics in addition to the degree of stenosis.
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Affiliation(s)
- Dara Azar
- Biomedical Engineering Program, College of Engineering and Computing, University of South Carolina, Columbia, SC, USA
| | - William M Torres
- Biomedical Engineering Program, College of Engineering and Computing, University of South Carolina, Columbia, SC, USA; Exponent, Inc, Philadelphia, PA, USA
| | - Lindsey A Davis
- Biomedical Engineering Program, College of Engineering and Computing, University of South Carolina, Columbia, SC, USA; Department of Cell Biology and Anatomy, University of South Carolina School of Medicine, Columbia, SC, USA
| | - Taylor Shaw
- Department of Cell Biology and Anatomy, University of South Carolina School of Medicine, Columbia, SC, USA
| | - John F Eberth
- Biomedical Engineering Program, College of Engineering and Computing, University of South Carolina, Columbia, SC, USA; Department of Cell Biology and Anatomy, University of South Carolina School of Medicine, Columbia, SC, USA
| | - Vijaya B Kolachalama
- Section of Computational Biomedicine, Department of Medicine, Boston University School of Medicine, Boston, MA, USA
| | - Susan M Lessner
- Biomedical Engineering Program, College of Engineering and Computing, University of South Carolina, Columbia, SC, USA; Department of Cell Biology and Anatomy, University of South Carolina School of Medicine, Columbia, SC, USA
| | - Tarek Shazly
- Biomedical Engineering Program, College of Engineering and Computing, University of South Carolina, Columbia, SC, USA; Department of Mechanical Engineering, College of Engineering and Computing, University of South Carolina, Columbia, SC, USA.
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25
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Selvan R, Petersen J, Pedersen JH, Bruijne M. Extracting tree structures in CT data by tracking multiple statistically ranked hypotheses. Med Phys 2019; 46:4431-4440. [DOI: 10.1002/mp.13711] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/16/2019] [Revised: 07/04/2019] [Accepted: 07/05/2019] [Indexed: 11/10/2022] Open
Affiliation(s)
- Raghavendra Selvan
- Department of Computer Science University of Copenhagen Copenhagen Denmark
| | - Jens Petersen
- Department of Computer Science University of Copenhagen Copenhagen Denmark
| | - Jesper H. Pedersen
- Department of Thoracic Surgery Rigshospitalet, University of Copenhagen Copenhagen Denmark
| | - Marleen Bruijne
- Department of Computer Science University of Copenhagen Copenhagen Denmark
- Biomedical Imaging Group Rotterdam, Department of Radiology and Nuclear Medicine Erasmus MC Rotterdam The Netherlands
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26
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Zhao J, Ai D, Yang Y, Song H, Huang Y, Wang Y, Yang J. Deep feature regression (DFR) for 3D vessel segmentation. ACTA ACUST UNITED AC 2019; 64:115006. [DOI: 10.1088/1361-6560/ab0eee] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/21/2023]
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27
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Directional fast-marching and multi-model strategy to extract coronary artery centerlines. Comput Biol Med 2019; 108:67-77. [DOI: 10.1016/j.compbiomed.2019.03.029] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/16/2018] [Revised: 03/29/2019] [Accepted: 03/30/2019] [Indexed: 11/18/2022]
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28
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Wolterink JM, van Hamersvelt RW, Viergever MA, Leiner T, Išgum I. Coronary artery centerline extraction in cardiac CT angiography using a CNN-based orientation classifier. Med Image Anal 2019; 51:46-60. [DOI: 10.1016/j.media.2018.10.005] [Citation(s) in RCA: 40] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/02/2018] [Revised: 10/05/2018] [Accepted: 10/18/2018] [Indexed: 01/16/2023]
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29
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CS-Net: Channel and Spatial Attention Network for Curvilinear Structure Segmentation. LECTURE NOTES IN COMPUTER SCIENCE 2019. [DOI: 10.1007/978-3-030-32239-7_80] [Citation(s) in RCA: 70] [Impact Index Per Article: 14.0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/12/2022]
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30
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Moriconi S, Zuluaga MA, Jager HR, Nachev P, Ourselin S, Cardoso MJ. Inference of Cerebrovascular Topology With Geodesic Minimum Spanning Trees. IEEE TRANSACTIONS ON MEDICAL IMAGING 2019; 38:225-239. [PMID: 30059296 PMCID: PMC6319031 DOI: 10.1109/tmi.2018.2860239] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 06/13/2018] [Accepted: 07/19/2018] [Indexed: 06/08/2023]
Abstract
A vectorial representation of the vascular network that embodies quantitative features-location, direction, scale, and bifurcations-has many potential cardio- and neuro-vascular applications. We present VTrails, an end-to-end approach to extract geodesic vascular minimum spanning trees from angiographic data by solving a connectivity-optimized anisotropic level-set over a voxel-wise tensor field representing the orientation of the underlying vasculature. Evaluating real and synthetic vascular images, we compare VTrails against the state-of-the-art ridge detectors for tubular structures by assessing the connectedness of the vesselness map and inspecting the synthesized tensor field. The inferred geodesic trees are then quantitatively evaluated within a topologically aware framework, by comparing the proposed method against popular vascular segmentation tool kits on clinical angiographies. VTrails potentials are discussed towards integrating groupwise vascular image analyses. The performance of VTrails demonstrates its versatility and usefulness also for patient-specific applications in interventional neuroradiology and vascular surgery.
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Hu X, Ding D, Chu D. Multiple Hidden Markov Model for Pathological Vessel Segmentation. BIOMED RESEARCH INTERNATIONAL 2018; 2018:9868215. [PMID: 30643827 PMCID: PMC6311274 DOI: 10.1155/2018/9868215] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 06/27/2018] [Revised: 09/12/2018] [Accepted: 11/28/2018] [Indexed: 11/27/2022]
Abstract
One of the obstacles that prevent the accurate delineation of vessel boundaries is the presence of pathologies, which results in obscure boundaries and vessel-like structures. Targeting this limitation, we present a novel segmentation method based on multiple Hidden Markov Models. This method works with a vessel axis + cross-section model, which constrains the classifier around the vessel. The vessel axis constraint gives our method the potential to be both physiologically accurate and computationally effective. Focusing on pathological vessels, we reap the benefits of the redundant information embedded in multiple vessel-specific features and the good statistical properties coming with Hidden Markov Model, to cover the widest possible spectrum of complex situations. The performance of our method is evaluated on synthetic complex-structured datasets, where we achieve a 91% high overlap ratio. We also validate the proposed method on a real challenging case, segmentation of pathological abdominal arteries. The performance of our method is promising, since our method yields better results than two state-of-the-art methods on both synthetic datasets and real clinical datasets.
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Affiliation(s)
- Xin Hu
- School of Computer Science and Technology, Harbin Institute of Technology at Weihai, Weihai 264209, China
| | - Deqiong Ding
- School of Computer Science and Technology, Harbin Institute of Technology, Harbin 150001, China
| | - Dianhui Chu
- School of Computer Science and Technology, Harbin Institute of Technology at Weihai, Weihai 264209, China
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Huang Q, Sun J, Ding H, Wang X, Wang G. Robust liver vessel extraction using 3D U-Net with variant dice loss function. Comput Biol Med 2018; 101:153-162. [DOI: 10.1016/j.compbiomed.2018.08.018] [Citation(s) in RCA: 41] [Impact Index Per Article: 6.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/22/2018] [Revised: 08/17/2018] [Accepted: 08/17/2018] [Indexed: 10/28/2022]
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Moccia S, De Momi E, El Hadji S, Mattos LS. Blood vessel segmentation algorithms - Review of methods, datasets and evaluation metrics. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2018; 158:71-91. [PMID: 29544791 DOI: 10.1016/j.cmpb.2018.02.001] [Citation(s) in RCA: 226] [Impact Index Per Article: 37.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/03/2017] [Revised: 12/23/2017] [Accepted: 02/02/2018] [Indexed: 05/09/2023]
Abstract
BACKGROUND Blood vessel segmentation is a topic of high interest in medical image analysis since the analysis of vessels is crucial for diagnosis, treatment planning and execution, and evaluation of clinical outcomes in different fields, including laryngology, neurosurgery and ophthalmology. Automatic or semi-automatic vessel segmentation can support clinicians in performing these tasks. Different medical imaging techniques are currently used in clinical practice and an appropriate choice of the segmentation algorithm is mandatory to deal with the adopted imaging technique characteristics (e.g. resolution, noise and vessel contrast). OBJECTIVE This paper aims at reviewing the most recent and innovative blood vessel segmentation algorithms. Among the algorithms and approaches considered, we deeply investigated the most novel blood vessel segmentation including machine learning, deformable model, and tracking-based approaches. METHODS This paper analyzes more than 100 articles focused on blood vessel segmentation methods. For each analyzed approach, summary tables are presented reporting imaging technique used, anatomical region and performance measures employed. Benefits and disadvantages of each method are highlighted. DISCUSSION Despite the constant progress and efforts addressed in the field, several issues still need to be overcome. A relevant limitation consists in the segmentation of pathological vessels. Unfortunately, not consistent research effort has been addressed to this issue yet. Research is needed since some of the main assumptions made for healthy vessels (such as linearity and circular cross-section) do not hold in pathological tissues, which on the other hand require new vessel model formulations. Moreover, image intensity drops, noise and low contrast still represent an important obstacle for the achievement of a high-quality enhancement. This is particularly true for optical imaging, where the image quality is usually lower in terms of noise and contrast with respect to magnetic resonance and computer tomography angiography. CONCLUSION No single segmentation approach is suitable for all the different anatomical region or imaging modalities, thus the primary goal of this review was to provide an up to date source of information about the state of the art of the vessel segmentation algorithms so that the most suitable methods can be chosen according to the specific task.
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Affiliation(s)
- Sara Moccia
- Department of Electronics, Information and Bioengineering, Politecnico di Milano, Milan, Italy; Department of Advanced Robotics, Istituto Italiano di Tecnologia, Genoa, Italy.
| | - Elena De Momi
- Department of Electronics, Information and Bioengineering, Politecnico di Milano, Milan, Italy
| | - Sara El Hadji
- Department of Electronics, Information and Bioengineering, Politecnico di Milano, Milan, Italy.
| | - Leonardo S Mattos
- Department of Advanced Robotics, Istituto Italiano di Tecnologia, Genoa, Italy
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Hu X, Cheng Y, Ding D, Chu D. Axis-Guided Vessel Segmentation Using a Self-Constructing Cascade-AdaBoost-SVM Classifier. BIOMED RESEARCH INTERNATIONAL 2018; 2018:3636180. [PMID: 29750151 PMCID: PMC5884412 DOI: 10.1155/2018/3636180] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 11/24/2017] [Revised: 02/04/2018] [Accepted: 02/13/2018] [Indexed: 11/23/2022]
Abstract
One major limiting factor that prevents the accurate delineation of vessel boundaries has been the presence of blurred boundaries and vessel-like structures. Overcoming this limitation is exactly what we are concerned about in this paper. We describe a very different segmentation method based on a cascade-AdaBoost-SVM classifier. This classifier works with a vessel axis + cross-section model, which constrains the classifier around the vessel. This has the potential to be both physiologically accurate and computationally effective. To further increase the segmentation accuracy, we organize the AdaBoost classifiers and the Support Vector Machine (SVM) classifiers in a cascade way. And we substitute the AdaBoost classifier with the SVM classifier under special circumstances to overcome the overfitting issue of the AdaBoost classifier. The performance of our method is evaluated on synthetic complex-structured datasets, where we obtain high overlap ratios, around 91%. We also validate the proposed method on one challenging case, segmentation of carotid arteries over real clinical datasets. The performance of our method is promising, since our method yields better results than two state-of-the-art methods on both synthetic datasets and real clinical datasets.
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Affiliation(s)
- Xin Hu
- School of Computer Science and Technology, Harbin Institute of Technology at Weihai, Weihai 264209, China
| | - Yuanzhi Cheng
- School of Computer Science and Technology, Harbin Institute of Technology, Harbin 150001, China
| | - Deqiong Ding
- Department of Mathematics, Harbin Institute of Technology at Weihai, Weihai 264209, China
| | - Dianhui Chu
- School of Computer Science and Technology, Harbin Institute of Technology at Weihai, Weihai 264209, China
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Nasr-Esfahani E, Karimi N, Jafari M, Soroushmehr S, Samavi S, Nallamothu B, Najarian K. Segmentation of vessels in angiograms using convolutional neural networks. Biomed Signal Process Control 2018. [DOI: 10.1016/j.bspc.2017.09.012] [Citation(s) in RCA: 51] [Impact Index Per Article: 8.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
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36
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Balancing the data term of graph-cuts algorithm to improve segmentation of hepatic vascular structures. Comput Biol Med 2018; 93:117-126. [DOI: 10.1016/j.compbiomed.2017.12.019] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/21/2017] [Revised: 12/21/2017] [Accepted: 12/21/2017] [Indexed: 12/21/2022]
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37
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Zhao Y, Zheng Y, Liu Y, Zhao Y, Luo L, Yang S, Na T, Wang Y, Liu J. Automatic 2-D/3-D Vessel Enhancement in Multiple Modality Images Using a Weighted Symmetry Filter. IEEE TRANSACTIONS ON MEDICAL IMAGING 2018; 37:438-450. [PMID: 28952938 DOI: 10.1109/tmi.2017.2756073] [Citation(s) in RCA: 37] [Impact Index Per Article: 6.2] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/07/2023]
Abstract
Automated detection of vascular structures is of great importance in understanding the mechanism, diagnosis, and treatment of many vascular pathologies. However, automatic vascular detection continues to be an open issue because of difficulties posed by multiple factors, such as poor contrast, inhomogeneous backgrounds, anatomical variations, and the presence of noise during image acquisition. In this paper, we propose a novel 2-D/3-D symmetry filter to tackle these challenging issues for enhancing vessels from different imaging modalities. The proposed filter not only considers local phase features by using a quadrature filter to distinguish between lines and edges, but also uses the weighted geometric mean of the blurred and shifted responses of the quadrature filter, which allows more tolerance of vessels with irregular appearance. As a result, this filter shows a strong response to the vascular features under typical imaging conditions. Results based on eight publicly available datasets (six 2-D data sets, one 3-D data set, and one 3-D synthetic data set) demonstrate its superior performance to other state-of-the-art methods.
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38
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Nasr-Esfahani E, Samavi S, Karimi N, Soroushmehr SMR, Ward K, Jafari MH, Felfeliyan B, Nallamothu B, Najarian K. Vessel extraction in X-ray angiograms using deep learning. ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2017; 2016:643-646. [PMID: 28268410 DOI: 10.1109/embc.2016.7590784] [Citation(s) in RCA: 27] [Impact Index Per Article: 3.9] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/07/2022]
Abstract
Coronary artery disease (CAD) is the most common type of heart disease which is the leading cause of death all over the world. X-ray angiography is currently the gold standard imaging technique for CAD diagnosis. These images usually suffer from low quality and presence of noise. Therefore, vessel enhancement and vessel segmentation play important roles in CAD diagnosis. In this paper a deep learning approach using convolutional neural networks (CNN) is proposed for detecting vessel regions in angiography images. Initially, an input angiogram is preprocessed to enhance its contrast. Afterward, the image is evaluated using patches of pixels and the network determines the vessel and background regions. A set of 1,040,000 patches is used in order to train the deep CNN. Experimental results on angiography images of a dataset show that our proposed method has a superior performance in extraction of vessel regions.
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Szilágyi SM, Popovici MM, Szilágyi L. Review. Automatic Segmentation Techniques of the Coronary Artery Using CT Images in Acute Coronary Syndromes. JOURNAL OF CARDIOVASCULAR EMERGENCIES 2017. [DOI: 10.1515/jce-2017-0002] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/27/2022] Open
Abstract
Abstract
Coronary artery disease represents one of the leading reasons of death worldwide, and acute coronary syndromes are their most devastating consequences. It is extremely important to identify the patients at risk for developing an acute myocardial infarction, and this goal can be achieved using noninvasive imaging techniques. Coronary computed tomography angiography (CCTA) is currently one of the most reliable methods used for assessing the coronary arteries; however, its use in emergency settings is sometimes limited due to time constraints. This paper presents the main characteristics of plaque vulnerability, the role of CCTA in the assessment of vulnerable plaques, and automatic segmentation techniques of the coronary artery tree based on CT angiography images. A detailed inventory of existing methods is given, representing the state-of-the-art of computational methods applied in vascular system segmentation, focusing on the current applications in acute coronary syndromes.
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Affiliation(s)
| | - Monica Marton Popovici
- Swedish Medical Center, Department of Internal Medicine and Critical Care, 21601, 76th Ave W, Edmonds, Washington , 98026, USA
| | - László Szilágyi
- Department of Electrical Engineering, Sapientia University, Tîrgu Mureș , Romania
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40
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Zhang Q, Chung ACS. 3D Vessel Segmentation Using Random Walker with Oriented Flux Analysis and Direction Coherence. ACTA ACUST UNITED AC 2016. [DOI: 10.1007/978-3-319-43775-0_25] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/18/2023]
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41
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Ilicak E, Cetin S, Bulut E, Oguz KK, Saritas EU, Unal G, Çukur T. Targeted vessel reconstruction in non-contrast-enhanced steady-state free precession angiography. NMR IN BIOMEDICINE 2016; 29:532-544. [PMID: 26854004 DOI: 10.1002/nbm.3497] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/15/2015] [Revised: 01/07/2016] [Accepted: 01/08/2016] [Indexed: 06/05/2023]
Abstract
Image quality in non-contrast-enhanced (NCE) angiograms is often limited by scan time constraints. An effective solution is to undersample angiographic acquisitions and to recover vessel images with penalized reconstructions. However, conventional methods leverage penalty terms with uniform spatial weighting, which typically yield insufficient suppression of aliasing interference and suboptimal blood/background contrast. Here we propose a two-stage strategy where a tractographic segmentation is employed to auto-extract vasculature maps from undersampled data. These maps are then used to incur spatially adaptive sparsity penalties on vascular and background regions. In vivo steady-state free precession angiograms were acquired in the hand, lower leg and foot. Compared with regular non-adaptive compressed sensing (CS) reconstructions (CSlow ), the proposed strategy improves blood/background contrast by 71.3 ± 28.9% in the hand (mean ± s.d. across acceleration factors 1-8), 30.6 ± 11.3% in the lower leg and 28.1 ± 7.0% in the foot (signed-rank test, P < 0.05 at each acceleration). The proposed targeted reconstruction can relax trade-offs between image contrast, resolution and scan efficiency without compromising vessel depiction.
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Affiliation(s)
- Efe Ilicak
- Department of Electrical and Electronics Engineering and the National Magnetic Resonance Research Center, Bilkent University, Ankara, Turkey
| | - Suheyla Cetin
- Faculty of Engineering and Natural Sciences, Sabanci University, Istanbul, Turkey
| | - Elif Bulut
- Hacettepe University Faculty of Medicine, Ankara, Turkey
| | | | - Emine Ulku Saritas
- Department of Electrical and Electronics Engineering and the National Magnetic Resonance Research Center, Bilkent University, Ankara, Turkey
| | - Gozde Unal
- Department of Computer Engineering, Istanbul Technical University, Istanbul, Turkey
| | - Tolga Çukur
- Department of Electrical and Electronics Engineering and the National Magnetic Resonance Research Center, Bilkent University, Ankara, Turkey
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