{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/a-combined-deep-learning-and-deformable-model","title":"A Combined Deep-Learning and Deformable-Model Approach to Fully Automatic Segmentation of the Left Ventricle in Cardiac MRI","arxiv_id":"1512.07951","date":"2015-12-25","proceeding":null,"authors":["M. R. Avendi","A. Kheradvar","H. Jafarkhani"],"abstract":"Segmentation of the left ventricle (LV) from cardiac magnetic resonance\nimaging (MRI) datasets is an essential step for calculation of clinical indices\nsuch as ventricular volume and ejection fraction. In this work, we employ deep\nlearning algorithms combined with deformable models to develop and evaluate a\nfully automatic segmentation tool for the LV from short-axis cardiac MRI\ndatasets. The method employs deep learning algorithms to learn the segmentation\ntask from the ground true data. Convolutional networks are employed to\nautomatically detect the LV chamber in MRI dataset. Stacked autoencoders are\nutilized to infer the shape of the LV. The inferred shape is incorporated into\ndeformable models to improve the accuracy and robustness of the segmentation.\nWe validated our method using 45 cardiac MR datasets taken from the MICCAI 2009\nLV segmentation challenge and showed that it outperforms the state-of-the art\nmethods. Excellent agreement with the ground truth was achieved. Validation\nmetrics, percentage of good contours, Dice metric, average perpendicular\ndistance and conformity, were computed as 96.69%, 0.94, 1.81mm and 0.86, versus\nthose of 79.2%-95.62%, 0.87-0.9, 1.76-2.97mm and 0.67-0.78, obtained by other\nmethods, respectively.","url_abs":"http://arxiv.org/abs/1512.07951v1","url_pdf":"http://arxiv.org/pdf/1512.07951v1.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"a-combined-deep-learning-and-deformable-model","repo_url":"https://github.com/alexattia/Medical-Image-Analysis","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":null}],"tasks":[{"task_slug":"lv-segmentation","task_name":"LV Segmentation"},{"task_slug":"segmentation","task_name":"Segmentation"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=1512.07951","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}