Robust Hydrocephalus Brain Segmentation via Globally and Locally Spatial Guidance

Abstract

Segmentation of brain regions for hydrocephalus MR images is pivotally important for quantitatively evaluating patients’ abnormalities. However, the brain image data obtained from hydrocephalus patients always have large deformations and lesion occupancies compared to the normal subjects. This leads to the disruption of the brain’s anatomical structure and the dramatic changes in the shape and location of the brain regions, which poses a significant challenge to the segmentation task. In this paper, we propose a novel segmentation framework, with two modules to better locate and segment these highly distorted brain regions. First, to provide the global anatomical structure information and the absolute position of target regions for segmentation, we use a dual-path registration network which is incorporated into the framework and trained simultaneously together. Second, we develop a novel Positional Correlation Attention Block (PCAB) to introduce the local prior information about the relative positional correlations between different regions, so that the segmentation network can be guided in locating the target regions. In this way, the segmentation framework can be trained with spatial guidance from both global and local positional priors to ensure the robustness of the segmentation. We evaluated our method on the brain MR data of hydrocephalus patients by segmenting 17 consciousness-related ROIs and demonstrated that the proposed method can achieve high performance on the image data with high variations of deformations. Source code is available at: https://github.com/JoeeYF/TBI-Brain-Region-Segmentation.