biostudies-literatureLiu YNIBIB NIH HHSNCRR NIH HHSNCI NIH HHSNational Institute of Biomedical Imaging and BioengineeringNIH HHS15161https://www.ebi.ac.uk/biostudies/studies/S-EPMC9452525biostudies-literatureUnknown12(1)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.Scientific reportsDeep learning multi-organ segmentation for whole mouse cryo-images including a comparison of 2D and 3D deep networks.PMC9452525R44CA213601R01 EB028635C06 RR012463R44 CA213601Liu YGargesha MScott BWilson DLTchilibou Wane AOfalseDeep learning multi-organ segmentation for whole mouse cryo-images including a comparison of 2D and 3D deep networks.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.2022-01-01T00:00:00Z2022 Sep2025-04-18T18:24:37.385Z2025-04-07T06:02:39.995ZS-EPMC94525253607108910.1038/s41598-022-19037-3