biostudies-literatureRahimpour MEuropean Research CouncilInstitut Curie959-969https://www.ebi.ac.uk/biostudies/studies/S-EPMC9889463biostudies-literatureUnknown33(2)<h4>Objectives</h4>To develop a visual ensemble selection of deep convolutional neural networks (CNN) for 3D segmentation of breast tumors using T1-weighted dynamic contrast-enhanced (T1-DCE) MRI.<h4>Methods</h4>Multi-center 3D T1-DCE MRI (n = 141) were acquired for a cohort of patients diagnosed with locally advanced or aggressive breast cancer. Tumor lesions of 111 scans were equally divided between two radiologists and segmented for training. The additional 30 scans were segmented independently by both radiologists for testing. Three 3D U-Net models were trained using either post-contrast images or a combination of post-contrast and subtraction images fused at either the image or the feature level. Segmentation accuracy was evaluated quantitatively using the Dice similarity coefficient European radiologyVisual ensemble selection of deep convolutional neural networks for 3D segmentation of breast tumors on dynamic contrast enhanced MRI.PMC9889463Bourse Curie M2 2018764458Rahimpour MMalhaire COrlhac FAkl PFrouin FSaint Martin MJKoole MfalseVisual ensemble selection of deep convolutional neural networks for 3D segmentation of breast tumors on dynamic contrast enhanced MRI.<h4>Objectives</h4>To develop a visual ensemble selection of deep convolutional neural networks (CNN) for 3D segmentation of breast tumors using T1-weighted dynamic contrast-enhanced (T1-DCE) MRI.<h4>Methods</h4>Multi-center 3D T1-DCE MRI (n = 141) were acquired for a cohort of patients diagnosed with locally advanced or aggressive breast cancer. Tumor lesions of 111 scans were equally divided between two radiologists and segmented for training. The additional 30 scans were segmented independently by both radiologists for testing. Three 3D U-Net models were trained using either post-contrast images or a combination of post-contrast and subtraction images fused at either the image or the feature level. Segmentation accuracy was evaluated quantitatively using the Dice similarity coefficient 2023-01-01T00:00:00Z2023 Feb2026-03-17T15:26:21.083Z2025-04-05T18:51:39.27ZS-EPMC98894633607426210.1007/s00330-022-09113-7