Watershed-based segmentation of the corpus callosum in diffusion MRI

Can incomplete silver standard labels improve performance of DTI-based volumetric segmentation of the corpus callosum?

“…Among the existing semi-automated and automated segmentation methods working directly on DTI, 15 most of them segment the CC on the midsagittal slice such as the ones proposed by Freitas et al, 21 Niogi et al 22 Nazem-Zadeh et al, 23 Luis-Garcıa et al, 24 Cover et al 25 and Herrera et al 26 For volumetric CC segmentation on DTI, there are only five automated methods: Rittner et al 27 extended Freitas et al' 28 approach and achieved a hierarchical segmentation by applying the 3D watershed using automatically selected markers; Kong et al 29 proposed a method to automatically learn an adaptive distance metric, through a semi-supervised learning model based on graphs; Wang et al 30 introduced the gray information of multiple atlases and the prior information of target shapes into the Active Shape Model (ASM) and proposed the Multi-Atlas Active Shape Model (MA-ASM) approach for segmentation of 7 Region of Interest (ROI), including the CC; Rodrigues et al 1 developed a volumetric segmentation method for the CC using Convolutional Neural Networks (CNN) using as input diffusion maps, such as fractional anisotropy (FA), mean diffusivity (MD) and mode of anisotropy (MO), separately and combined; Rodrigues et al 31 developed a subsequent method based on 3D convolutions, presenting better performance in comparison to the 2D model.…”