{"database":"biostudies-literature","file_versions":[],"scores":null,"additional":{"submitter":["Liu Y"],"funding":["NIBIB NIH HHS","NCRR NIH HHS","NCI NIH HHS","National Institute of Biomedical Imaging and Bioengineering","NIH HHS"],"pagination":["15161"],"full_dataset_link":["https://www.ebi.ac.uk/biostudies/studies/S-EPMC9452525"],"repository":["biostudies-literature"],"omics_type":["Unknown"],"volume":["12(1)"],"pubmed_abstract":["Cryo-imaging provided 3D whole-mouse microscopic color anatomy and fluorescence images that enables biotechnology applications (e.g., stem cells and metastatic cancer). In this report, we compared three methods of organ segmentation: 2D U-Net with 2D-slices and 3D U-Net with either 3D-whole-mouse or 3D-patches. We evaluated the brain, thymus, lung, heart, liver, stomach, spleen, left and right kidney, and bladder. Training with 63 mice, 2D-slices had the best performance, with median Dice scores of > 0.9 and median Hausdorff distances of < 1.2 mm in eightfold cross validation for all organs, except bladder, which is a problem organ due to variable filling and poor contrast. Results were comparable to those for a second analyst on the same data. Regression analyses were performed to fit learning curves, which showed that 2D-slices can succeed with fewer samples. Review and editing of 2D-slices segmentation results reduced human operator time from ~ 2-h to ~ 25-min, with reduced inter-observer variability. As demonstrations, we used organ segmentation to evaluate size changes in liver disease and to quantify the distribution of therapeutic mesenchymal stem cells in organs. With a 48-GB GPU, we determined that extra GPU RAM improved the performance of 3D deep learning because we could train at a higher resolution."],"journal":["Scientific reports"],"pubmed_title":["Deep learning multi-organ segmentation for whole mouse cryo-images including a comparison of 2D and 3D deep networks."],"pmcid":["PMC9452525"],"funding_grant_id":["R44CA213601","R01 EB028635","C06 RR012463","R44 CA213601"],"pubmed_authors":["Liu Y","Gargesha M","Scott B","Wilson DL","Tchilibou Wane AO"],"additional_accession":[]},"is_claimable":false,"name":"Deep learning multi-organ segmentation for whole mouse cryo-images including a comparison of 2D and 3D deep networks.","description":"Cryo-imaging provided 3D whole-mouse microscopic color anatomy and fluorescence images that enables biotechnology applications (e.g., stem cells and metastatic cancer). In this report, we compared three methods of organ segmentation: 2D U-Net with 2D-slices and 3D U-Net with either 3D-whole-mouse or 3D-patches. We evaluated the brain, thymus, lung, heart, liver, stomach, spleen, left and right kidney, and bladder. Training with 63 mice, 2D-slices had the best performance, with median Dice scores of > 0.9 and median Hausdorff distances of < 1.2 mm in eightfold cross validation for all organs, except bladder, which is a problem organ due to variable filling and poor contrast. Results were comparable to those for a second analyst on the same data. Regression analyses were performed to fit learning curves, which showed that 2D-slices can succeed with fewer samples. Review and editing of 2D-slices segmentation results reduced human operator time from ~ 2-h to ~ 25-min, with reduced inter-observer variability. As demonstrations, we used organ segmentation to evaluate size changes in liver disease and to quantify the distribution of therapeutic mesenchymal stem cells in organs. With a 48-GB GPU, we determined that extra GPU RAM improved the performance of 3D deep learning because we could train at a higher resolution.","dates":{"release":"2022-01-01T00:00:00Z","publication":"2022 Sep","modification":"2025-04-18T18:24:37.385Z","creation":"2025-04-07T06:02:39.995Z"},"accession":"S-EPMC9452525","cross_references":{"pubmed":["36071089"],"doi":["10.1038/s41598-022-19037-3"]}}