Multi-channel multi-scale fully convolutional network for 3D perivascular spaces segmentation in 7T MR images.

ABSTRACT: Accurate segmentation of perivascular spaces (PVSs) is an important step for quantitative study of PVS morphology. However, since PVSs are the thin tubular structures with relatively low contrast and also the number of PVSs is often large, it is challenging and time-consuming for manual delineation of PVSs. Although several automatic/semi-automatic methods, especially the traditional learning-based approaches, have been proposed for segmentation of 3D PVSs, their performance often depends on the hand-crafted image features, as well as sophisticated preprocessing operations prior to segmentation (e.g., specially defined regions-of-interest (ROIs)). In this paper, a novel fully convolutional neural network (FCN) with no requirement of any specified hand-crafted features and ROIs is proposed

SUBMITTER: Lian C 

PROVIDER: S-EPMC6430123 | biostudies-literature | 2018 May

REPOSITORIES: biostudies-literature