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