What type of study is this?

This is a Quantitative Study study.

September 17, 2025

TotalSpineSeg: Robust Spine Segmentation and Labeling Across Multiple MRI Contrasts

Key Points

The model achieves a segmentation Dice score of 0.84, demonstrating robust performance across various MRI sequences.
Labeling accuracy is exceptional at 0.99, ensuring precise identification of vertebrae in diverse imaging contexts.
An iterative algorithm processes anatomical landmarks through a three-step approach with two nnUNetV21 models for effective segmentation.
TotalSpineSeg enhances the diagnosis of spinal pathologies, supporting improved clinical decision-making in patient care.

Abstract

Motivation: Precise segmentation and labeling of vertebrae is essential to properly assess spine and spinal cord diseases such as spinal cord injury, degenerative diseases and scoliosis. Goal(s): Develop an automatic tool to segment and identify vertebrae on a wide range of MRI protocols, including different MRI sequences, contrasts, fields-of-view and resolutions. Approach: A three-step process including two nnUNetV21 models and an iterative labeling algorithm was used to segment and identify vertebrae using anatomical landmarks. Results: The model generalizes well across MRI sequences, with a segmentation Dice score of 0.84 and an average labeling accuracy of 0.99. Impact: TotalSpineSeg could enhance clinical workflows by providing automatic vertebrae segmentation, improving the diagnosis of various spinal pathologies and supporting informed clinical decision-making. It is available on GitHub (https://github.com/neuropoly/totalspineseg) and in Spinal Cord Toolbox v.6.514.

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Cite This Study

Warszawer et al. (Tue,) studied this question.

synapsesocial.com/papers/68d4597b31b076d99fa5cd1c https://doi.org/https://doi.org/10.58530/2025/0624

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