{"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/detection-and-segmentation-of-the-left","title":"Detection and segmentation of the Left Ventricle in Cardiac MRI using Deep Learning","arxiv_id":"1801.02171","date":"2018-01-07","proceeding":null,"authors":["Alexandre Attia","Sharone Dayan"],"abstract":"Manual segmentation of the Left Ventricle (LV) is a tedious and meticulous\ntask that can vary depending on the patient, the Magnetic Resonance Images\n(MRI) cuts and the experts. Still today, we consider manual delineation done by\nexperts as being the ground truth for cardiac diagnosticians. Thus, we are\nreviewing the paper - written by Avendi and al. - who presents a combined\napproach with Convolutional Neural Networks, Stacked Auto-Encoders and\nDeformable Models, to try and automate the segmentation while performing more\naccurately. Furthermore, we have implemented parts of the paper (around three\nquarts) and experimented both the original method and slightly modified\nversions when changing the architecture and the parameters.","url_abs":"http://arxiv.org/abs/1801.02171v1","url_pdf":"http://arxiv.org/pdf/1801.02171v1.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":"detection-and-segmentation-of-the-left","repo_url":"https://github.com/alexattia/Medical-Image-Analysis","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"none","reach":null}],"tasks":[{"task_slug":"segmentation","task_name":"Segmentation"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"syntology_url":null,"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}