RATNUS: Rapid, Automatic Thalamic Nuclei Segmentation using Multimodal MRI inputs

TL;DR

RATNUS is a novel deep learning method that uses multimodal MRI inputs, including synthetic T1 images and diffusion features, to accurately segment 13 thalamic nuclei, outperforming existing tools.

Contribution

This work introduces RATNUS, a new multimodal MRI-based segmentation approach with a unified labeling scheme for thalamic nuclei, achieving higher accuracy than prior methods.

Findings

Achieves 87.19% true positive rate in nuclei segmentation

Outperforms FreeSurfer and THOMAS in accuracy

Uses synthetic T1 images with diffusion features for better contrast

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.