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Machine Learning for Assessment of Coronary Artery Disease in Cardiac CT: A Survey

Nils Hampe, Jelmer M Wolterink, Sanne G M van Velzen, Tim Leiner, Ivana Išgum
2019 Frontiers in Cardiovascular Medicine  
We summarize ML methods for detection and characterization of atherosclerotic plaque as well as anatomically and functionally significant coronary artery stenosis.  ...  Cardiac computed tomography (CT) allows rapid visualization of the heart and coronary arteries with high spatial resolution.  ...  Coronary centerline extraction via optimal flow paths and CNN path pruning. In: Ourselin S, Joskowicz L, Sabuncu MR, Unal G, Wells W, editors.  ... 
doi:10.3389/fcvm.2019.00172 pmid:32039237 pmcid:PMC6988816 fatcat:tsq6vf3vi5a4rjxoxzqy7xd5tu

Automated Deep Learning Analysis of Angiography Video Sequences for Coronary Artery Disease [article]

Chengyang Zhou, Thao Vy Dinh, Heyi Kong, Jonathan Yap, Khung Keong Yeo, Hwee Kuan Lee, Kaicheng Liang
2021 arXiv   pre-print
We propose a 3-stage automated analysis method consisting of key frame extraction, vessel segmentation, and stenosis measurement.  ...  We report an automated analysis pipeline based on deep learning to rapidly and objectively assess coronary angiograms, highlight coronary vessels of interest, and quantify potential stenosis.  ...  First, the segmentation mask was skeletonized [31] , [32] and pruned (Fig. 1E a-b) (Appendix A, Algorithm 1) to produce a centerline.  ... 
arXiv:2101.12505v1 fatcat:hbra654dkvdbtivz4oc4mpvmua

Deep Learning for Cardiac Image Segmentation: A Review

Chen Chen, Chen Qin, Huaqi Qiu, Giacomo Tarroni, Jinming Duan, Wenjia Bai, Daniel Rueckert
2020 Frontiers in Cardiovascular Medicine  
ultrasound and major anatomical structures of interest (ventricles, atria, and vessels).  ...  Finally, we discuss the challenges and limitations with current deep learning-based approaches (scarcity of labels, model generalizability across different domains, interpretability) and suggest potential  ...  ACKNOWLEDGMENTS We would like to thank our colleagues: Karl Hahn, Qingjie Meng, James Batten, and Jonathan Passerat-Palmbach who provided the insight and expertise that greatly assisted the work, and also  ... 
doi:10.3389/fcvm.2020.00025 pmid:32195270 pmcid:PMC7066212 fatcat:iw7xpnltn5cgbn5ullq2ldy3nq

Deep learning for cardiac image segmentation: A review [article]

Chen Chen, Chen Qin, Huaqi Qiu, Giacomo Tarroni, Jinming Duan, Wenjia Bai, Daniel Rueckert
2019 arXiv   pre-print
ultrasound (US) and major anatomical structures of interest (ventricles, atria and vessels).  ...  Finally, we discuss the challenges and limitations with current deep learning-based approaches (scarcity of labels, model generalizability across different domains, interpretability) and suggest potential  ...  transformer network CTA Vessel CNN as pre-/post-processing Gülsün et al. (2016) CNN as path pruning CTA coronary artery centerline Guo et al. (2019) multi-task FCN with a minimal patch extractor  ... 
arXiv:1911.03723v1 fatcat:cwsq5hiaebgkza5ktmtyw553je

2021 Index IEEE Transactions on Image Processing Vol. 30

2021 IEEE Transactions on Image Processing  
The primary entry includes the coauthors' names, the title of the paper or other item, and its location, specified by the publication abbreviation, year, month, and inclusive pagination.  ...  The Subject Index contains entries describing the item under all appropriate subject headings, plus the first author's name, the publication abbreviation, month, and year, and inclusive pages.  ...  Network: Weakly Supervised Accurate Coronary Lumen Segmentation Using Centerline Constraint.  ... 
doi:10.1109/tip.2022.3142569 fatcat:z26yhwuecbgrnb2czhwjlf73qu

Robust PCA Unrolling Network for Super-resolution Vessel Extraction in X-ray Coronary Angiography [article]

Binjie Qin, Haohao Mao, Yiming Liu, Jun Zhao, Yisong Lv, Yueqi Zhu, Song Ding, Xu Chen
2022 arXiv   pre-print
Although robust PCA has been increasingly adopted to extract vessels from X-ray coronary angiography (XCA) images, challenging problems such as inefficient vessel-sparsity modelling, noisy and dynamic  ...  flowing in the XCA-imaged vessels.  ...  ACKNOWLEDGEMENTS The authors thank all the cited authors for providing the source codes used in this work and the anonymous reviewers for their valuable comments on the manuscript.  ... 
arXiv:2204.08466v2 fatcat:msgakedt4na6xmgzokaskno25a

Comparative Analysis of Vessel Segmentation Techniques in Retinal Images

Azhar Imran, Jianqiang Li, Yan Pei, Ji-Jiang Yang, Qing Wang
2019 IEEE Access  
Moreover, we compared these approaches to the dataset, evaluation metrics, pre-processing and post-processing steps, feature extraction, segmentation methods, and induced results.  ...  Miscellaneous methods: These methods are mostly evaluated from 2004 to 2017 on STARE and DRIVE dataset with average accuracy and AUC is about 0.94 and 0.96 respectively.  ...  This is further intended to find the local information and a vessel profile model path in which not only vessel centerline but the width of every vessel is extracted correctly.  ... 
doi:10.1109/access.2019.2935912 fatcat:64ycsdgecza4tkq3m5c7qs2r7m

Sparsely Activated Networks: A new method for decomposing and compressing data [article]

Paschalis Bizopoulos
2021 arXiv   pre-print
representation length and consist of interpretable kernels.  ...  We then present and define two activation functions (Identity, ReLU) as base of reference and three sparse activation functions (top-k absolutes, Extrema-Pool indices, Extrema) as candidate structures  ...  [108] Mehmet A Gülsün, Gareth Funka-Lea, Puneet Sharma, Saikiran Rapaka, and Yefeng Zheng. Coronary centerline extraction via optimal flow paths and cnn path pruning.  ... 
arXiv:1911.00400v2 fatcat:4gdsy2iywvgitdugodkdfqdzg4

Deep Learning-Based Cardiac Imaging Data Measurement and Its Application in Diagnosis of Sudden Cardiac Death

Z H Li, N G Liu, H W Dong, L J Li, H H He, L H Lin, Q Liu, M Z Yang
As postmortem CT imaging plays a more and more important role in the appraisal of cause of death and cardiopathology research, the application of deep learning such as artificial intelligence technology  ...  to analyze vast amounts of cardiac imaging data has provided a possibility for forensic identification and scientific research workers to conduct precise diagnosis and quantitative analysis of cardiac  ...  Coronary centerline extraction via optimal flow [39] TAO Q,YAN W,WANG Y,et al. Deep learning- Med, 2019, 25(1): 24-29. doi: 10.1038/s41591-018-M, et al.  ... 
doi:10.12116/j.issn.1004-5619.2021.410503 pmid:34726010 fatcat:lzq7g56qxvc6hf5wuot2ok3fnq

Deformability-induced effects of red blood cells in flow [article]

David Alexander Kihm, Universität Des Saarlandes
CNN for the feature extraction.  ...  Thus, the finding of an appropriate optimization strategy and an eligible error function form two remaining issues to train the CNN.  ... 
doi:10.22028/d291-35054 fatcat:mih6em3t6fd4jfkre5qmqf5ebq

Structure-guided registration in learning based image analysis

Matthew Chung Hai Lee, Ben Glocker
Firstly, we utilise image registration to perform image segmentation via template deformation, the registration of some prior shape model with an image.  ...  Image registration is a key component in many medical image analysis pipelines and is useful in general computer vision applications.  ...  Real World Applications Consider a real world coronary artery analysis pipeline as depicted in Figure 4 .15, where first an automatic centerline is extracted and corrected.  ... 
doi:10.25560/79296 fatcat:rldmsq3fefabpkurupwmi3yu3u