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Updated: May 4, 2026

Diffusion Tensor Magnetic Resonance Imaging in Chronic Spinal Cord Compression

Diffusion Tensor Magnetic Resonance Imaging in Chronic Spinal Cord Compression

Published on: May 7, 2019

Segmentation of spinal rootlets across MRI contrasts with RootletSeg.

Kateřina Krejčí^1,2, Jiří Chmelík¹, Sandrine Bédard²

¹Department of Biomedical Engineering, FEEC, Brno University of Technology, Brno, Czechia.

Scientific Reports

|May 2, 2026

Summary

This summary is machine-generated.

This study introduces RootletSeg, an open-source deep learning tool for automatically segmenting spinal nerve rootlets (C2-T1) from various MRI scans. This method aids in spinal level estimation and lesion classification.

Keywords:

Deep learning Nerve rootlets Segmentation Spinal cord Spinal levels

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Area of Science:

Medical Imaging
Neuroscience
Artificial Intelligence

Background:

Spinal nerve rootlet segmentation is crucial for accurate spinal level estimation, lesion classification, and therapeutic interventions.
Existing methods for spinal nerve rootlet segmentation are often manual and time-consuming.
Automated segmentation can significantly improve the efficiency and consistency of spinal imaging analyses.

Purpose of the Study:

To develop and validate a deep learning-based method for automatic segmentation of C2-T1 dorsal and ventral spinal nerve rootlets.
To assess the performance of the developed method across different MRI contrasts and datasets.
To provide an open-source tool for researchers and clinicians.

Main Methods:

A deep learning model, termed RootletSeg, was developed using 93 MRI scans from 50 healthy adults.
The model was trained and evaluated on various MRI sequences including 3T T2-weighted (T2w) and 7T MP2RAGE (T1w INV1, INV2, UNIT1).
Performance was quantified using the Dice score across different MRI contrasts.

Main Results:

RootletSeg achieved a mean Dice score ranging from 0.62 ± 0.10 (T1w-INV1) to 0.67 ± 0.09 (T1w-INV2).
The model demonstrated accurate segmentation of C2-T1 spinal rootlets across T1w and T2w MRI contrasts.
The segmentation enabled direct determination of spinal levels from MRI scans.

Conclusions:

RootletSeg provides an effective and automated solution for spinal nerve rootlet segmentation across various MRI modalities.
The open-source nature of RootletSeg facilitates its integration into diverse downstream neuroimaging analyses.
This tool has the potential to advance research in spinal cord imaging and related clinical applications.