Active Gradient Guidance Based Susceptibility and Magnitude Information Complete Network for Basal Ganglia Segmentation

Motivation: Accurate segmentation of basal ganglia is a crucial prerequisite for subsequent clinical practice and research. The boundaries of BG remain challenging to segment especially when dealing with data affected by severe artifacts. Goal(s): This work aims to propose an automatic BG segmentation method with radiologist comparable accuracy and high inference speed. Approach: An active gradient guidance-based susceptibility and magnitude information complete network(AGNet). With newly designed modules, AGNet can efficiently capture the inter-slice information and exploit it as attention guidance to facilitate the segmentation process. Results: AGNet has superior segment accuracy over existing methods with ADSC=0.874 and AHD=2.010, especially near boundaries of target VOI. Impact: The proposed model achieves more accurate segmentation at the boundary contour. Automatic and precise segmentation of basal ganglia is a prerequisite for the quantification of tissue magnetic susceptibility analysis and can serve as a fundamental tool for neurodegenerative disease research.