{"database":"biostudies-literature","file_versions":[],"scores":null,"additional":{"submitter":["Bai W"],"funding":["British Heart Foundation","Medical Research Council","Wellcome Trust","Engineering and Physical Sciences Research Council"],"pagination":["65"],"full_dataset_link":["https://www.ebi.ac.uk/biostudies/studies/S-EPMC6138894"],"repository":["biostudies-literature"],"omics_type":["Unknown"],"volume":["20(1)"],"pubmed_abstract":["<h4>Background</h4>Cardiovascular resonance (CMR) imaging is a standard imaging modality for assessing cardiovascular diseases (CVDs), the leading cause of death globally. CMR enables accurate quantification of the cardiac chamber volume, ejection fraction and myocardial mass, providing information for diagnosis and monitoring of CVDs. However, for years, clinicians have been relying on manual approaches for CMR image analysis, which is time consuming and prone to subjective errors. It is a major clinical challenge to automatically derive quantitative and clinically relevant information from CMR images.<h4>Methods</h4>Deep neural networks have shown a great potential in image pattern recognition and segmentation for a variety of tasks. Here we demonstrate an automated analysis method for C"],"journal":["Journal of cardiovascular magnetic resonance : official journal of the Society for Cardiovascular Magnetic Resonance"],"pubmed_title":["Automated cardiovascular magnetic resonance image analysis with fully convolutional networks."],"pmcid":["PMC6138894"],"funding_grant_id":["203553","MC_PC_17228","203553/Z/16/Z","EP/N014529/1","MR/L016311/1","EP/P001009/1","PG/14/89/31194","MC_QA137853","203553/Z/Z","EP/N50869X/1"],"pubmed_authors":["Tarroni G","Fung K","Carapella V","Rueckert D","Sinclair M","Neubauer S","Sanghvi MM","Oktay O","Lee AM","Petersen SE","Bai W","Suzuki H","Glocker B","Paiva JM","Zemrak F","Aung N","Kainz B","Kim YJ","Piechnik SK","Lukaschuk E","Rajchl M","Matthews PM","Vaillant G"],"additional_accession":[]},"is_claimable":false,"name":"Automated cardiovascular magnetic resonance image analysis with fully convolutional networks.","description":"<h4>Background</h4>Cardiovascular resonance (CMR) imaging is a standard imaging modality for assessing cardiovascular diseases (CVDs), the leading cause of death globally. CMR enables accurate quantification of the cardiac chamber volume, ejection fraction and myocardial mass, providing information for diagnosis and monitoring of CVDs. However, for years, clinicians have been relying on manual approaches for CMR image analysis, which is time consuming and prone to subjective errors. It is a major clinical challenge to automatically derive quantitative and clinically relevant information from CMR images.<h4>Methods</h4>Deep neural networks have shown a great potential in image pattern recognition and segmentation for a variety of tasks. Here we demonstrate an automated analysis method for C","dates":{"release":"2018-01-01T00:00:00Z","publication":"2018 Sep","modification":"2025-04-22T06:46:07.96Z","creation":"2019-03-26T23:56:16Z"},"accession":"S-EPMC6138894","cross_references":{"pubmed":["30217194"],"doi":["10.1186/s12968-018-0471-x"]}}