Vertebrae and Intervertebral Discs Segmentation using Deep Learning-Based Model in Disability Analysis

Nizar Alsharif^1*Rajit Nair²Theyazn H.H Aldhyani³Nesren Farhah⁴Sultan Ahmad^5*Abdullah H. Al-Nefaie⁶

• ¹Department of Computer Engineering and Science, Al-baha University, Al-baha 42331, Saudi Arabia, Al-baha, Saudi Arabia
• ²VIT Bhopal University, Sehore, India
• ³Applied College, King Faisal University, P.O. Box 400, Al-Ahsa 31982, Saudi Arabia, Al-Ahsa, Saudi Arabia
• ⁴Department of Health Informatics, College of Health Science, Saudi Electronic University, Riyadh 11673, Saudi Arabia, Riyadh, Saudi Arabia
• ⁵Department of Computer Science, College of Computer Engineering and Sciences, Prince Sattam Bin Abdulaziz University, Alkharj, Saudi Arabia
• ⁶King Salman Center for Disability Research, Riyadh 11614, Saudi Arabia, Riyadh, Saudi Arabia

Segmentation of vertebrae and intervertebral discs (IVDs) is a cornerstone of the diagnosis and treatment of disorders affecting the spine. Yet, most methodologies, especially CNN-based, mostly treat vertebrae and discs independently, missing out on the potential of their anatomical relationships. To fill this gap, we present a two-stage deep learning framework that incorporates structural dependency modeling to automate spine segmentation in T2-weighted MR images. In the framework, the components of the spine are modeled as nodes of a graph, with anatomical relationships stored in the system's adjacency matrix. A 3D Graph Convolutional Segmentation Network (GCSN) is first used to perform coarse multi-class segmentation, leveraging the relationships between vertebrae and discs. Then, a 2D ResNet refinement network is used to enhance boundary resolution. The model was tested on volumetric MR data of 218 subjects. The average Dice similarity coefficient (DSC) across 10 vertebrae was 87.32%, 87.78% across 9 intervertebral discs, and 87.49% across 19 structures in the spinal column, showing exemplary segmentation performance. The result shows that the segmentation consistency and accuracy have improved significantly due to the use of the anatomical dependencies through the graph-based learning approach. The proposed system provides a safe and highly effective automated system for parsing the spine and can be clinically used for diagnosing and planning the treatments for spinal disorders.