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A deep learning framework for comprehensive segmentation of deep grey nuclei.

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This study introduces THOMASINA, a deep learning pipeline for fast and accurate segmentation of deep brain structures from MRI scans. The method significantly reduces processing time and improves segmentation accuracy, paving the way for large-scale neuroimaging studies.

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Area of Science:

  • Neuroimaging
  • Artificial Intelligence
  • Medical Image Analysis

Background:

  • Accurate segmentation of deep grey matter structures (thalamus, basal nuclei) is crucial for understanding neurological disorders.
  • Challenges include poor MRI contrast, lengthy processing, and fragmented tools.

Purpose of the Study:

  • To develop a deep learning pipeline (THOMASINA) for comprehensive subcortical segmentation.
  • To enable segmentation from standard T1-weighted (T1w) and white-matter-nulled (WMn) MRI.

Main Methods:

  • Trained multiple 3D deep learning models (SwinUNETR, DiNTS, SegResNet) using labels from a state-of-the-art multi-atlas method.
  • Employed cropped volumes for training and tested on diverse datasets.
  • Utilized a synthesis step to generate WMn-like contrast from T1w MRI.

Main Results:

  • SegResNet achieved the highest performance (mean Dice 0.89 in-domain, 0.85 out-of-domain), outperforming other models.
  • Synthetic WMn contrast yielded segmentation comparable to actual WMn images.
  • Reduced segmentation time from minutes to seconds per subject.

Conclusions:

  • THOMASINA provides a fast, reproducible, and scalable solution for subcortical segmentation using standard T1w MRI.
  • Addresses key deployment barriers and supports biomarker discovery in large-scale imaging.
  • Demonstrates robustness across field strengths, vendors, and disease cohorts.