1. RATNUS: Rapid, Automatic Thalamic Nuclei Segmentation using Multimodal MRI inputs
- Author
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Feng, Anqi, Bian, Zhangxing, Dewey, Blake E., Colinco, Alexa Gail, Zhuo, Jiachen, and Prince, Jerry L.
- Subjects
Electrical Engineering and Systems Science - Image and Video Processing - Abstract
Accurate segmentation of thalamic nuclei is important for better understanding brain function and improving disease treatment. Traditional segmentation methods often rely on a single T1-weighted image, which has limited contrast in the thalamus. In this work, we introduce RATNUS, which uses synthetic T1-weighted images with many inversion times along with diffusion-derived features to enhance the visibility of nuclei within the thalamus. Using these features, a convolutional neural network is used to segment 13 thalamic nuclei. For comparison with other methods, we introduce a unified nuclei labeling scheme. Our results demonstrate an 87.19% average true positive rate (TPR) against manual labeling. In comparison, FreeSurfer and THOMAS achieve TPRs of 64.25% and 57.64%, respectively, demonstrating the superiority of RATNUS in thalamic nuclei segmentation.
- Published
- 2024