Boundary-Aware Network With Topological Consistency Constraint for Optic Chiasm Segmentation

Abstract

Optic chiasm is a structure which is easily compressed by the tumors leading to different degrees of visual field defect and visual disturbance. When the optic chiasm is compressed, segmentation of the optic chiasm from MRI images is helpful for prognosis prediction and radiotherapy planning. However, due to the ambiguity of the optic chiasm boundary and the neglect of the anatomical structure consistency, the performance of the existing methods is limited. In this work, a Boundary-Aware Network (BANet) with topological consistency constraint is proposed for automated segmentation of optic chiasm. BANet constrained by topology loss leverages the complementary information between boundary feature and segmentation feature to effectively improve segmentation performance and topological consistency. To evaluate the effectiveness of the proposed method, a real-world specialized optic chiasm segmentation dataset is constructed. The experimental results demonstrate that the proposed method achieves higher segmentation accuracy compared with the state-of-the-art method.