Keywords: Segmentation, Segmentation, Brain tissue
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.
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