Deformation-Aware Segmentation Network Robust to Motion Artifacts for Brain Tissue Segmentation using Disentanglement Learning

• URL: http://arxiv.org/abs/2412.03922v1
• Date: Thu, 05 Dec 2024 06:52:42 GMT
• Title: Deformation-Aware Segmentation Network Robust to Motion Artifacts for Brain Tissue Segmentation using Disentanglement Learning
• Authors: Sunyoung Jung, Yoonseok Choi, Mohammed A. Al-masni, Minyoung Jung, Dong-Hyun Kim,
• Abstract summary: Motion artifacts are a significant challenge in Magnetic Resonance Imaging (MRI)<n>This study proposes a novel deep learning framework that demonstrates superior performance in both motion correction and robust brain tissue segmentation.<n>In-vivo experiments on pediatric motion data demonstrate that our proposed framework outperforms state-of-the-art methods in segmenting motion-corrupted MRI scans.
• Score: 5.354351782195383
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• 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.

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