Automated Artery Localization and Vessel Wall Segmentation Using Tracklet Refinement and Polar Conversion
| Autor: | Thomas S. Hatsukami, Gador Canton, Niranjan Balu, Jenq-Neng Hwang, Jie Sun, Daniel S. Hippe, Rui Li, Li Chen, Xihai Zhao, Chun Yuan |
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| Rok vydání: | 2020 |
| Předmět: |
General Computer Science
Computer science Feature extraction Initialization 030204 cardiovascular system & hematology Convolutional neural network Article 030218 nuclear medicine & medical imaging vessel wall segmentation 03 medical and health sciences 0302 clinical medicine Region of interest medicine General Materials Science Segmentation Computer vision Artificial neural network medicine.diagnostic_test business.industry General Engineering artery localization Magnetic resonance imaging Image segmentation cardiovascular system Artery detection lcsh:Electrical engineering. Electronics. Nuclear engineering Artificial intelligence atherosclerosis business polar conversion tracklet refinement lcsh:TK1-9971 |
| Zdroj: | IEEE Access, Vol 8, Pp 217603-217614 (2020) IEEE Access |
| ISSN: | 2169-3536 |
| DOI: | 10.1109/access.2020.3040616 |
| Popis: | Quantitative analysis of blood vessel wall structures is important to study atherosclerotic diseases and assess cardiovascular event risks. To achieve this, accurate identification of vessel luminal and outer wall contours is needed. Computer-assisted tools exist, but manual preprocessing steps, such as region of interest identification and/or boundary initialization, are still needed. In addition, prior knowledge of the ring shape of vessel walls has not been fully explored in designing segmentation methods. In this work, a fully automated artery localization and vessel wall segmentation system is proposed. A tracklet refinement algorithm was adapted to robustly identify the artery of interest from a neural network-based artery centerline identification architecture. Image patches were extracted from the centerlines and converted in a polar coordinate system for vessel wall segmentation. The segmentation method used 3D polar information and overcame problems such as contour discontinuity, complex vessel geometry, and interference from neighboring vessels. Verified by a large (>32000 images) carotid artery dataset collected from multiple sites, the proposed system was shown to better automatically segment the vessel wall than traditional vessel wall segmentation methods or standard convolutional neural network approaches. In addition, a segmentation uncertainty score was estimated to effectively identify slices likely to have errors and prompt manual confirmation of the segmentation. This robust vessel wall segmentation system has applications in different vascular beds and will facilitate vessel wall feature extraction and cardiovascular risk assessment. |
| Databáze: | OpenAIRE |
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