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Abstract #4847

Fully Automatic Vertebrae and Spinal Cord Segmentation Using a Hybrid Approach Combining nnU-Net and Iterative Algorithm

Yehuda Warszawer1,2,3, Nathan Molinier4,5, Jan Valosek4,5, Emanuel Shirbint1, Pierre-Louis Benveniste4,5, Anat Achiron1,6, Arman Eshaghi7,8,9, and Julien Cohen-Adad4,5,10,11
1Multiple Sclerosis Center, Sheba Medical Center, Ramat-Gan, Israel, 2Arrow Program for Medical Research Education, Sheba Medical Center, Ramat-Gan, Israel, 3Adelson School of Medicine, Ariel University, Ariel, Israel, 4NeuroPoly Lab, Institute of Biomedical Engineering, Polytechnique Montreal, Montreal, QC, Canada, 5Mila - Quebec AI Institute, Montreal, QC, Canada, 6Faculty of Medicine, Tel-Aviv University, Tel-Aviv, Israel, 7Queen Square Multiple Sclerosis Centre, Department of Neuroinflammation, University College London, London, United Kingdom, 8Queen Square Institute of Neurology, Faculty of Brain Sciences, University College London, London, United Kingdom, 9Centre for Medical Image Computing (CMIC), Department of Computer Science, Faculty of Engineering Sciences, University College London, London, United Kingdom, 10Functional Neuroimaging Unit, CRIUGM, Université de Montréal, Montreal, QC, Canada, 11Centre de recherche du CHU Sainte-Justine, Université de Montréal, Montreal, QC, Canada

Synopsis

Keywords: AI/ML Software, Segmentation

Motivation: 3D visualisation of the spinal cord and vertebrae anatomy is critical for treatment planning and assessment of cord atrophy in neurodegenerative and traumatic diseases.

Goal(s): Develop a fully automatic segmentation of the whole spinal cord, vertebrae and discs.

Approach: The hybrid method combines a nnU-Net with an iterative processing algorithm with Spinal Cord Toolbox to conveniently generate ground truth labels. We used 3D T1w and T2w scans from three different databases.

Results: A validation Dice score of 0.928 was obtained (averaged across contrasts, classes and datasets), suggesting promising segmentation accuracy and capabilities for generalisation given the use of multi-site/multi-vendor datasets.

Impact: The fully automatic segmentation of the spine and spinal cord will pinpoint pathologies at specific vertebrae level, offering visualization for surgery preparation. This could also refine segmentation of substructures like multiple sclerosis lesions and tumors, inspiring solutions for related issues.

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Abstract

Keywords