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Abstract #1853

Improving Subcortical Segmentation in Brain MRI Using Knowledge Distillation to Enhance Robustness Against Motion Artifacts

Changmin Ryu1, Sunyoung Jung1, Yoonseok Choi1, and Dong-Hyun Kim1
1Department of Electrical and Electronic Engineering, Yonsei University, Seoul, Korea, Republic of

Synopsis

Keywords: Analysis/Processing, Segmentation, Brain subcortical regions, Motion artifacts, Knowledge distillation

Motivation: Accurate segmentation of subcortical brain regions in MRI is challenging due to motion artifacts that distort structural details and affect subsequent analyses, necessitating improved approaches for segmentation reliability.

Goal(s): To develop a knowledge distillation framework that reduces the influence of motion artifacts on MRI segmentation, thus enhancing subcortical accuracy without the need for motion correction preprocessing.

Approach: A teacher model trained on motion-free data guides a student model trained on motion-corrupted data, improving segmentation accuracy without complex motion correction.

Results: The framework improved Dice Similarity Coefficients in subcortical regions, demonstrating enhanced segmentation performance and robustness on motion-corrupted data.

Impact: This approach improves MRI segmentation of motion-corrupted data, supporting reliable subcortical analysis without complex preprocessing. It provides a method for cleaner, artifact-resistant segmentation, presenting significant applications both in neurodevelopmental and neurodegenerative disease research.

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Keywords