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Automated Deep Learning-based Segmentation of the Dentate Nucleus Using Quantitative Susceptibility Mapping MRI.

Diogo H Shiraishi¹, Susmita Saha^2,3, Isaac M Adanyeguh⁴

¹Department of Neurology, School of Medical Sciences, University of Campinas (Unicamp), Rua Vital Brasil, 89-99, Cidade Universitária "Zeferino Vaz", Campinas, São Paulo, Brazil 13083-888.

Radiology. Artificial Intelligence

|August 6, 2025

Summary

This summary is machine-generated.

A new deep learning tool accurately segments the dentate nucleus (DN) from brain quantitative susceptibility mapping (QSM) images. This automated method shows high performance and generalizability, outperforming existing tools for neurological condition research.

Keywords:

Brain/Brain Stem Computer Applications–3D Convolutional Neural Network Image Postprocessing MR Imaging Segmentation Supervised Learning Volume Analysis

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Area of Science:

Neuroimaging
Artificial Intelligence in Medicine
Medical Image Analysis

Background:

Accurate segmentation of the dentate nucleus (DN) is crucial for understanding neurological disorders.
Quantitative susceptibility mapping (QSM) offers valuable insights into brain tissue properties.
Deep learning (DL) has shown promise in automating complex medical image segmentation tasks.

Purpose of the Study:

To develop and validate a deep learning-based tool for automated segmentation of the dentate nucleus (DN) on brain MRI quantitative susceptibility mapping (QSM) images.
To assess the performance and generalizability of the developed DL model across diverse datasets.

Main Methods:

A retrospective study collected brain QSM images from healthy controls and patients with cerebellar ataxia or multiple sclerosis across nine international datasets.
A two-step deep learning approach, involving a localization model followed by a segmentation model (nnU-Net framework), was employed.
Performance was evaluated using intraclass correlation coefficient (ICC), Dice scores, and Pearson correlation coefficients against manual delineations.

Main Results:

The developed DL model achieved high Dice scores for left and right DN segmentation (0.90 ± 0.03 and 0.89 ± 0.04, respectively).
The automated tool significantly outperformed the leading existing automated method in external testing (mean Dice scores 0.86 vs 0.57 for left DN, 0.84 vs 0.58 for right DN).
The model demonstrated strong generalizability across unseen datasets, with automated segmentations highly correlated with manual annotations (r = 0.74 for left DN, r = 0.48 for right DN).

Conclusions:

The proposed deep learning model provides accurate and efficient segmentation of the dentate nucleus from brain QSM images.
This tool has the potential to advance research in neurological conditions affecting the cerebellum.
The publicly available model (https://github.com/art2mri/DentateSeg) facilitates wider adoption and research.