Abstract

Motivation: Motion artifacts in MRI scans present challenges by causing blurred images with tissue-like appearances, significantly complicating the tissue segmentation process. Goal(s): Our goal is to achieve accurate brain tissue segmentation even in the presence of motion artifacts. Approach: We propose a brain tissue segmentation method robust to motion artifacts, that generates a motion deformation map and a prediction mask for brain tissue segmentation. The motion deformation map serves as an indicator within the segmentation network, aiding in the identification of regions impacted by motion artifacts. Results: Our method demonstrates superior performance compared to other segmentation models, especially when dealing with motion-corrupted data. Impact: We propose a motion-robust segmentation network that incorporates prior motion knowledge via a motion estimation network. By employing a multi-task learning approach involving joint motion estimation and segmentation networks, we improve brain tissue segmentation by recovering incorrectly segmented structures.