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3 April 2024 Boosting substantia nigra segmentation from T2 weighted MRI via test-time normalization and distance-reweighted loss
Tao Hu, Hayato Itoh, Masahiro Oda, Shinji Saiki, Nobutaka Hattori, Koji Kamagata, Wataru Sako, Kei-ichi Ishikawa, Shigeki Aoki, Kensaku Mori
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Proceedings Volume 12927, Medical Imaging 2024: Computer-Aided Diagnosis; 129270O (2024) https://doi.org/10.1117/12.3008521
Event: SPIE Medical Imaging, 2024, San Diego, California, United States
Abstract
Substantia nigra (SN) has been reported as significantly related to the progression of Parkinson’s Disease (PD). Fully automated segmentation of SN is an important step for developing an interpretable computer-aided diagnosis system for PD. Based on the deep learning techniques, this paper proposes a novel distance-reweighted loss function and combines it with the test-time normalization (TTN) to boost the fully automated SN segmentation accuracy from low contrast T2 weighted MRI. The proposed loss encourages the model to focus on the suspicious regions with vague boundaries, and the involved TTN narrows the gap between an input MRI volume and the reference MRI volumes in test-time. The results showed that both the proposed loss and TTN could help improve the segmentation accuracy. By combining the proposed loss and TTN, the averaged Dice coefficient achieved 70.90% from T2 weighted MRI, compared to 68.17% by the baseline method.
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(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.
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Tao Hu, Hayato Itoh, Masahiro Oda, Shinji Saiki, Nobutaka Hattori, Koji Kamagata, Wataru Sako, Kei-ichi Ishikawa, Shigeki Aoki, and Kensaku Mori "Boosting substantia nigra segmentation from T2 weighted MRI via test-time normalization and distance-reweighted loss", Proc. SPIE 12927, Medical Imaging 2024: Computer-Aided Diagnosis, 129270O (3 April 2024); https://doi.org/10.1117/12.3008521
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KEYWORDS
Magnetic resonance imaging
Voxels
Image segmentation
Brain
Parkinson disease
Deep learning
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