# Segmenting Thalamic Nuclei: T1 Maps Provide a Reliable and Efficient Solution

arXiv: https://arxiv.org/abs/2508.12508 · HTML: https://mlsanity.com/paper/2508.12508/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 11 of its 37 references that are in the map.
- First posted on arXiv 17 Aug 2025.

## Builds on (most cited first, 11 of 11)

- [Grad-CAM: Visual Explanations from Deep Networks via Gradient-based Localization](https://mlsanity.com/paper/1610.02391/) (2016)
- [Dropout as a Bayesian Approximation: Representing Model Uncertainty in Deep Learning](https://mlsanity.com/paper/1506.02142/) (2015)
- [Axiomatic Attribution for Deep Networks](https://mlsanity.com/paper/1703.01365/) (2017)
- [3D U-Net: Learning Dense Volumetric Segmentation from Sparse Annotation](https://mlsanity.com/paper/1606.06650/) (2016)
- [Exploring Uncertainty Measures in Deep Networks for Multiple Sclerosis Lesion Detection and Segmentation](https://mlsanity.com/paper/1808.01200/) (2018)
- [Towards safe deep learning: accurately quantifying biomarker uncertainty in neural network predictions](https://mlsanity.com/paper/1806.08640/) (2018)
- [A probabilistic atlas of the human thalamic nuclei combining ex vivo MRI and histology](https://mlsanity.com/paper/1806.08634/) (2018)
- [Segmenting thalamic nuclei from manifold projections of multi-contrast MRI](https://mlsanity.com/paper/2301.06114/) (2023)
- [Label Propagation via Random Walk for Training Robust Thalamus Nuclei Parcellation Model from Noisy Annotations](https://mlsanity.com/paper/2303.17706/) (2023)
- [Revisiting registration-based synthesis: A focus on unsupervised MR image synthesis](https://mlsanity.com/paper/2402.12288/) (2024)
- [RATNUS: Rapid, Automatic Thalamic Nuclei Segmentation using Multimodal MRI inputs](https://mlsanity.com/paper/2409.06897/) (2024)

## Abstract (arXiv)

Accurate thalamic nuclei segmentation is crucial for understanding neurological diseases, brain functions, and guiding clinical interventions. However, the optimal inputs for segmentation remain unclear. This study systematically evaluates multiple MRI contrasts, including MPRAGE and FGATIR sequences, quantitative PD and T1 maps, and multiple T1-weighted images at different inversion times (multi-TI), to determine the most effective inputs. For multi-TI images, we employ a gradient-based saliency analysis with Monte Carlo dropout and propose an Overall Importance Score to select the images contributing most to segmentation. A 3D U-Net is trained on each of these configurations. Results show that T1 maps alone achieve strong quantitative performance and superior qualitative outcomes, while PD maps offer no added value. These findings underscore the value of T1 maps as a reliable and efficient input among the evaluated options, providing valuable guidance for optimizing imaging protocols when thalamic structures are of clinical or research interest.