{"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/multi-views-fusion-cnn-for-left-ventricular","title":"Multi-views Fusion CNN for Left Ventricular Volumes Estimation on Cardiac MR Images","arxiv_id":"1804.03008","date":"2018-04-09","proceeding":null,"authors":["Gongning Luo","Suyu Dong","Kuanquan Wang","WangMeng Zuo","Shaodong Cao","Henggui Zhang"],"abstract":"Left ventricular (LV) volumes estimation is a critical procedure for cardiac\ndisease diagnosis. The objective of this paper is to address direct LV volumes\nprediction task. Methods: In this paper, we propose a direct volumes prediction\nmethod based on the end-to-end deep convolutional neural networks (CNN). We\nstudy the end-to-end LV volumes prediction method in items of the data\npreprocessing, networks structure, and multi-views fusion strategy. The main\ncontributions of this paper are the following aspects. First, we propose a new\ndata preprocessing method on cardiac magnetic resonance (CMR). Second, we\npropose a new networks structure for end-to-end LV volumes estimation. Third,\nwe explore the representational capacity of different slices, and propose a\nfusion strategy to improve the prediction accuracy. Results: The evaluation\nresults show that the proposed method outperforms other state-of-the-art LV\nvolumes estimation methods on the open accessible benchmark datasets. The\nclinical indexes derived from the predicted volumes agree well with the ground\ntruth (EDV: R2=0.974, RMSE=9.6ml; ESV: R2=0.976, RMSE=7.1ml; EF: R2=0.828, RMSE\n=4.71%). Conclusion: Experimental results prove that the proposed method may be\nuseful for LV volumes prediction task. Significance: The proposed method not\nonly has application potential for cardiac diseases screening for large-scale\nCMR data, but also can be extended to other medical image research fields","url_abs":"http://arxiv.org/abs/1804.03008v1","url_pdf":"http://arxiv.org/pdf/1804.03008v1.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":"multi-views-fusion-cnn-for-left-ventricular","repo_url":"https://github.com/luogongning/Multi-views-fusion","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"none","reach":null}],"tasks":[{"task_slug":"prediction","task_name":"Prediction"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}