Figure 8:

Examples of segmentation overlaid with MR images. Each row shows a representative 2D cross-sectional slice from images of subjects with a given status (healthy/diseased). The red rectangles highlight regions with segmentation errors. In the control subject, the incorrect segmentation between TP and Sol in nnUNet_80 is mostly alleviated in FilterNet+_80 and corrected in DeepLOGISMOS_80. In the Pre-DM1 example, the shrunk TP segmentation in FilterNet+_80 is guided by DeepLOGISMOS_80 to the correct position at the boundaries. In the severe DM1 case, the infiltrates of Gas into the subcutaneous adipose tissue visible for both nnUNet and FilterNet+ methods is solved by the Deep LOGISMOS method. Similarly, in the JDM case, disconnected TP by FilterNet+ method is substantially alleviated by Deep LOGISMOS. Throughout all the examples, segmentation improvements are noticeable for each more advanced method from the nnUNet to Deep LOGISMOS methods in the same dataset, for which muscle compartments segmented are increasingly better agreeing with the ground truth. In the DM1 and JDM case, DeepLOGISMOS_80 even outperforms nnUNet_350 and FilterNet+_350. Best viewed in color.