Deformation-Aware Segmentation Network Robust to Motion Artifacts for Brain Tissue Segmentation using Disentanglement Learning
Journal:
arXiv
Published Date:
Dec 5, 2024
Abstract
Motion artifacts caused by prolonged acquisition time are a significant
challenge in Magnetic Resonance Imaging (MRI), hindering accurate tissue
segmentation. These artifacts appear as blurred images that mimic tissue-like
appearances, making segmentation difficult. This study proposes a novel deep
learning framework that demonstrates superior performance in both motion
correction and robust brain tissue segmentation in the presence of artifacts.
The core concept lies in a complementary process: a disentanglement learning
network progressively removes artifacts, leading to cleaner images and
consequently, more accurate segmentation by a jointly trained motion estimation
and segmentation network. This network generates three outputs: a
motioncorrected image, a motion deformation map that identifies
artifact-affected regions, and a brain tissue segmentation mask. This
deformation serves as a guidance mechanism for the disentanglement process,
aiding the model in recovering lost information or removing artificial
structures introduced by the artifacts. Extensive in-vivo experiments on
pediatric motion data demonstrate that our proposed framework outperforms
state-of-the-art methods in segmenting motion-corrupted MRI scans.
