Segmentation of mouse tibia on MRI using deep learning U-Net models

Abstract

We are developing deep learning models for the segmentation of mouse tibia in MRI scans by utilizing three U-Net architectures: Attention, Inception, and basic U-Net, on a data set of 32 mice with 158 MRI scans. The data set was split into training (23 mice, 108 scans), validation (3 mice, 17 scans), and test (6 mice, 33 scans) sets. Two expert annotators (EA1 and EA2) provided manual 3D segmentations of the tibia on the MRI scans. EA1 provided outlines on all MRI scans, which were used as the reference for the training, validation, and testing of U-net models. EA2 provided outlines on the validation and test set, which were used for the assessment of inter-observer reference variability. The model performance was evaluated based on the average Jaccard index (%AJI), average volume intersection ratio (%AVI), average volume error (%AVE), and average Hausdorff distance (AHD, mm). For the test set, the %AJI with reference to EA1 was 83.45 ± 5.11 for the Attention U-Net, 83.05 ± 6.21 for the Inception U-Net, and 83.38 ± 5.36 for the basic U-Net. The %AJI was 80.70 ± 2.91 for EA1 versus EA2 and 79.70 ± 6.28 for Attention U-Net versus EA2. The variability between the U-Net models and EA1 and EA2 references was similar to the variability between EA1 and EA2. All 3 U-Net architectures achieved similar performances with the Attention U-Net performing marginally better.