{"id":422712,"date":"2017-09-01T15:04:27","date_gmt":"2017-09-01T22:04:27","guid":{"rendered":"https:\/\/cm-edgetun.pages.dev\/en-us\/research\/?post_type=msr-research-item&p=422712"},"modified":"2018-10-16T20:13:35","modified_gmt":"2018-10-17T03:13:35","slug":"deepvessel-retinal-vessel-segmentation-via-deep-learning-conditional-random-field","status":"publish","type":"msr-research-item","link":"https:\/\/cm-edgetun.pages.dev\/en-us\/research\/publication\/deepvessel-retinal-vessel-segmentation-via-deep-learning-conditional-random-field\/","title":{"rendered":"DeepVessel: Retinal Vessel Segmentation via Deep Learning and Conditional Random Field"},"content":{"rendered":"
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Retinal vessel segmentation is a fundamental step for various ocular imaging applications. In this paper, we formulate the retinal vessel segmentation problem as a boundary detection task and solve it using a novel deep learning architecture. Our method is based on two key ideas: (1) applying a multi-scale and multi-level Convolutional Neural Network (CNN) with a side-output layer to learn a rich hierarchical representation, and (2) utilizing a Conditional Random Field (CRF) to model the long-range interactions between pixels. We combine the CNN and CRF layers into an integrated deep network called DeepVessel<\/em>. Our experiments show that the DeepVessel system achieves state-of-the-art retinal vessel segmentation performance on the DRIVE, STARE, and CHASE_DB1 datasets with an efficient running time.<\/p>\n<\/section>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n<\/article>\n<\/div>\n","protected":false},"excerpt":{"rendered":"

Retinal vessel segmentation is a fundamental step for various ocular imaging applications. In this paper, we formulate the retinal vessel segmentation problem as a boundary detection task and solve it using a novel deep learning architecture. Our method is based on two key ideas: (1) applying a multi-scale and multi-level Convolutional Neural Network (CNN) with […]<\/p>\n","protected":false},"featured_media":0,"template":"","meta":{"msr-url-field":"","msr-podcast-episode":"","msrModifiedDate":"","msrModifiedDateEnabled":false,"ep_exclude_from_search":false,"_classifai_error":"","msr-author-ordering":null,"msr_publishername":"","msr_publisher_other":"","msr_booktitle":"","msr_chapter":"","msr_edition":"Medical Image Computing and Computer Assisted Intervention (MICCAI)","msr_editors":"","msr_how_published":"","msr_isbn":"","msr_issue":"","msr_journal":"","msr_number":"","msr_organization":"","msr_pages_string":"132-139","msr_page_range_start":"132","msr_page_range_end":"139","msr_series":"","msr_volume":"9901","msr_copyright":"","msr_conference_name":"Medical Image Computing and Computer Assisted Intervention 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