CATNUS: Coordinate-Aware Thalamic Nuclei Segmentation Using T1-Weighted MRI

TL;DR

CATNUS is a novel deep learning method that accurately segments 13 thalamic nuclei from various MRI sequences, demonstrating improved accuracy, robustness, and generalization over existing techniques, thus aiding clinical and research applications.

Contribution

The paper introduces CATNUS, a coordinate-aware 3D U-Net model with enhanced localization for thalamic nuclei segmentation, supporting multiple MRI sequences and showing superior performance and robustness.

Findings

Outperforms established methods like FreeSurfer, THOMAS, and HIPS-THOMAS.

Demonstrates strong test-retest reliability across datasets.

Shows robust generalization across different scanners and imaging conditions.

Abstract

Accurate segmentation of thalamic nuclei from magnetic resonance images is important due to the distinct roles of these nuclei in overall brain function and to their differential involvement in neurological and psychiatric disorders. However, segmentation remains challenging given the small size of many nuclei, limited intrathalamic contrast and image resolution, and inter-subject anatomical variability. In this work, we present CATNUS (Coordinate-Aware Thalamic Nuclei Segmentation), segmenting 13 thalamic nuclei (or nuclear groups) using a 3D U-Net architecture enhanced with coordinate convolution layers, which provide more precise localization of both large and small nuclei. To support broad clinical applicability, we provide pre-trained model variants that can operate on quantitative T1 maps as well as on widely used magnetization-prepared rapid gradient echo (MPRAGE) and fast gray matter acquisition T1 inversion recovery (FGATIR) sequences. We benchmarked CATNUS against established methods, including FreeSurfer, THOMAS and HIPS-THOMAS, demonstrating improved segmentation accuracy and robust test-retest reliability across multiple nuclei. Furthermore, CATNUS demonstrated strong out-of-distribution generalization on traveling-subject datasets spanning multiple scanners, field strengths, and vendors, producing reliable and anatomically coherent segmentations across diverse acquisition conditions. Overall, CATNUS provides an accurate and generalizable solution for thalamic nuclei segmentation, with strong potential to facilitate large-scale neuroimaging studies and support real-world clinical assessment.