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

arXiv: https://arxiv.org/abs/2409.06897 · HTML: https://mlsanity.com/paper/2409.06897/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 10 Sep 2024.

## Built on by (most cited first, 1 of 1)

- [Segmenting Thalamic Nuclei: T1 Maps Provide a Reliable and Efficient Solution](https://mlsanity.com/paper/2508.12508/) (2025)

## Abstract (arXiv)

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.