Multi-Method and Multi-Atlas Segmentation Fusion for Delineation of Thigh Muscle Groups in 3D Water-Fat Separated MRI

Segmentation is an essential tool for quantification and characterization of tissue properties, with applications ranging from assessment of body composition, disease diagnosis, to development of imaging biomarkers. In this work, we propose a multi-method and multi-atlas methodology for automated segmentation of functional muscle groups in 3D Dixon MR images of the mid-thigh. The functional muscle groups addressed in this paper lie anatomically close to each other, that makes segmentation an arduous task for accuracy. We propose an approach that uses anatomical mappings enabling delineation of adjacent muscle groups that are difficult to separate using conventional intensity-based patterns only. We segment the four functional muscle groups of the thigh in both legs by multi-atlas anatomical mappings and fuse the labels to improve delineation accuracy. We investigate the fusion of segmentation from multiple atlases and multiple deformable registration methods. For performance evaluation we applied cross-validation by excluding the scans that served as templates in our framework and report DSC values on the remaining test scans. We evaluated four individual deformable models, free-form deformation (FFD), symmetric normalization (SYN), symmetric diffeomorphic demons (SDD), and Voxelmorph (VXM), and the joint multi-method fusion. Multi-atlas and multi-method fusion produced the top average DSC of 0.795 over all muscles on the test scans.