{"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/left-ventricle-quantification-through-spatio","title":"Left ventricle quantification through spatio-temporal CNNs","arxiv_id":"1808.07967","date":"2018-08-23","proceeding":null,"authors":["Alejandro Debus","Enzo Ferrante"],"abstract":"Cardiovascular diseases are among the leading causes of death globally.\nCardiac left ventricle (LV) quantification is known to be one of the most\nimportant tasks for the identification and diagnosis of such pathologies. In\nthis paper, we propose a deep learning method that incorporates 3D\nspatio-temporal convolutions to perform direct left ventricle quantification\nfrom cardiac MR sequences. Instead of analysing slices independently, we\nprocess stacks of temporally adjacent slices by means of 3D convolutional\nkernels which fuse the spatio-temporal information, incorporating the temporal\ndynamics of the heart to the learned model. We show that incorporating such\ninformation by means of spatio-temporal convolutions into standard LV\nquantification architectures improves the accuracy of the predictions when\ncompared with single-slice models, achieving competitive results for all\ncardiac indices and significantly breaking the state of the art (Xue et al.,\n2018, MedIA) for cardiac phase estimation.","url_abs":"http://arxiv.org/abs/1808.07967v1","url_pdf":"http://arxiv.org/pdf/1808.07967v1.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":"left-ventricle-quantification-through-spatio","repo_url":"https://github.com/alejandrodebus/SpatioTemporalCNN_lvquan","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"pytorch","reach":null},{"paper_slug":"left-ventricle-quantification-through-spatio","repo_url":"https://github.com/alejandrodebus/Indices-Net","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":null},{"paper_slug":"left-ventricle-quantification-through-spatio","repo_url":"https://github.com/alejandrodebus/IndicesNet","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":null}],"tasks":[],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}