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

arXiv 2508.12508 ↗First posted 17 Aug 2025eess.IV cs.CV q-bio.QMcited by 0

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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.

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References · 11

  1. 2016Grad-CAM: Visual Explanations from Deep Networks via Gradient-based Localizationcited by 6,733
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  3. 2017Axiomatic Attribution for Deep Networkscited by 3,384
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  5. 2018Exploring Uncertainty Measures in Deep Networks for Multiple Sclerosis Lesion Detection and Segmentationcited by 146
  6. 2018Towards safe deep learning: accurately quantifying biomarker uncertainty in neural network predictionscited by 25
  7. 2018A probabilistic atlas of the human thalamic nuclei combining ex vivo MRI and histologycited by 10
  8. 2023Segmenting thalamic nuclei from manifold projections of multi-contrast MRIcited by 2
  9. 2023Label Propagation via Random Walk for Training Robust Thalamus Nuclei Parcellation Model from Noisy Annotationscited by 1
  10. 2024Revisiting registration-based synthesis: A focus on unsupervised MR image synthesiscited by 1
  11. 2024RATNUS: Rapid, Automatic Thalamic Nuclei Segmentation using Multimodal MRI inputscited by 1

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