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Domain-agnostic segmentation of thalamic nuclei from joint structural and diffusion MRI
Arxiv
|May 19, 2023
Summary
This study introduces a novel Convolutional Neural Network (CNN) for segmenting thalamic nuclei from MRI scans. The method accurately maps brain structures regardless of data resolution, improving diagnostic capabilities for neurological diseases.
Area of Science:
- Neuroimaging
- Computational Neuroscience
- Medical Image Analysis
Background:
- The human thalamus, a critical subcortical structure, contains numerous nuclei with distinct functions.
- Accurate in vivo segmentation of thalamic nuclei using MRI is challenging due to faint boundary contrasts.
- Existing segmentation tools often struggle to generalize across different diffusion MRI acquisition parameters.
Conclusions:
- The presented CNN offers the first resolution-independent and acquisition-generalizable method for thalamic nuclei segmentation.
- This tool has the potential to significantly advance in vivo research on thalamic nuclei and related neurological conditions.
- Public availability of the implementation facilitates broader adoption and research in the field.
