DH-GAC: deep hierarchical context fusion network with modified geodesic active contour for multiple neurofibromatosis segmentation

Xiangqiong Wu, Guanghua Tan, Bin Pu, Mingxing Duan, Wenli Cai

Abstract: Delineating accurately and simultaneously all lesions is vital and challenging for computer-aided diagnosis for multiple neurofibromatosis (NF). However, existing CNN-based segmentation methods paid little attention to weak boundaries. Moreover, due to the intensity-inhomogeneous distribution of medical images, the ambiguous boundaries, and highly variable locations, sizes and shapes of the lesions, delineating multiple lesions simultaneously remains quite challenging. To address these challenges, we introduce a novel end-to-end segmentation framework of multiple NF, deep hierarchical geodesic active contour (DH-GAC). It leverages the elaborately designed deep hierarchical context fusion network (DH-CFN) to improve the generalization and robustness of DH-GAC, and the modified geodesic active contour (MGAC) to delineate precisely all lesions as much as possible. Specifically, it employs DH-CFN to predict specific parameter maps of each image for MGAC and feeds them into the energy function of MGAC to delineate NF lesions, which makes DH-GAC end-to-end trainable. Moreover, to improve the generalization of DH-GAC, we adopt two different settings to initialize the surface for DH-GAC. Experimental results demonstrate that DH-GAC not only improves the segmentation precision, but also overcomes the intrinsic drawback of classical geodesic active contour in boundary delineation.