A Novel End-to-end Joint Reconstruction and Segmentation Interaction Network for MRI

Motivation: For magnetic resonance imaging (MRI) applications, rapid imaging and automatic segmentation of target tissues are critical. However, most existing methods barely consider MR image segmentation in fast imaging scenarios. Goal(s): Our goal is to simultaneously achieve high scanning acceleration and accurate multi-class tissue segmentation results under a unified framework. Approach: We propose a novel multi-task method with a novel interaction module to reconstruct undersampled MR images based on modified ISTA-Net and simultaneously segment tissues based on lightweight U-Net. Results: Experiments on cardiac and knee datasets demonstrate that our method outperforms existing state-of-the-art multi-task approaches for joint MR image reconstruction and segmentation. Impact: The proposed multi-task interaction method can effectively achieve high scanning acceleration and accurate segmentation results simultaneously, which can further expand the application of MR in clinical disease diagnosis.