CT-based segmentation of thoracic vertebrae using deep learning and computation of the kyphotic angle

Syed Ahmed Nadeem; Alejandro P. Comellas; Indranil Guha; Elizabeth A. Regan; Eric A. Hoffman; Punam K. Saha

doi:10.1117/12.2613065

4 April 2022 CT-based segmentation of thoracic vertebrae using deep learning and computation of the kyphotic angle

Syed Ahmed Nadeem, Alejandro P. Comellas, Indranil Guha, Elizabeth A. Regan, Eric A. Hoffman, Punam K. Saha

Proceedings Volume 12036, Medical Imaging 2022: Biomedical Applications in Molecular, Structural, and Functional Imaging; 1203616 (2022) https://doi.org/10.1117/12.2613065
Event: SPIE Medical Imaging, 2022, San Diego, California, United States

Abstract

Spinal degeneration and vertebral fractures are common among the elderly adversely impacting mobility, quality of life, lung function, fracture risk, and mortality. Segmentation of individual vertebrae from computed tomography (CT) imaging is crucial for studying spine degeneration, vertebral fractures, and bone density with aging and their mechanistic links with demographics, lifestyle factors, and comorbidities. We present an automated method to segment individual vertebral bodies (T1-L1) and compute the kyphotic angle of the spine from chest CT images. A three-dimensional U-Net was developed, optimized, and trained to generate a voxellevel vertebral probability map from a chest CT scan. Multi-parametric thresholding was applied on the probability map to segment individual vertebrae by iteratively relaxing the probability threshold value, while avoiding fusion among adjacent vertebrae. The kyphotic angle was computed using two orthogonal planes on the spine centerline at the inter-vertebral spaces T3-T4 and T12-L1 and a common sagittal plane. Total lung capacity (TLC) chest CT scans from baseline visits of the COPDGene Iowa cohort were used for our experiments. The U-Net method was trained and validated using 40 scans and tested on a separate set of 100 scans. Segmentation of individual vertebrae achieved a mean Dice score of 0.93 as compared to manual segmentation, and the kyphotic angle computation method produced a linear correlation of 0.88 (r-value) with manual measurements. This method provides a fully automated tool to study different mechanistic pathways of age-related spine modeling and vertebral fractures in retrospective datasets available from large multi-site chest related studies.

Conference Presentation

Citation Download Citation

Syed Ahmed Nadeem, Alejandro P. Comellas, Indranil Guha, Elizabeth A. Regan, Eric A. Hoffman, and Punam K. Saha "CT-based segmentation of thoracic vertebrae using deep learning and computation of the kyphotic angle", Proc. SPIE 12036, Medical Imaging 2022: Biomedical Applications in Molecular, Structural, and Functional Imaging, 1203616 (4 April 2022); https://doi.org/10.1117/12.2613065

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