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Previous literature has investigated the biomechanical response of healthy and degenerative discs, but the biomechanical response of suboptimal healthy intervertebral discs received less attention. The purpose was to compare the biomechanical responses and risk of herniation of young healthy, suboptimal healthy, and degenerative intervertebral discs. A cervical spine model was established and validated using the finite element method. Suboptimal healthy, mildly, moderately, and severely degenerative disc models were developed. Disc height deformation, range of motion, intradiscal pressure, and von Mises stress in annulus fibrosus were analyzed by applying a moment of 4 Nm in flexion, extension, lateral bending, and axial rotation with 100 N compressive loads. Disc height deformation in young healthy, suboptimal healthy, mildly, moderately, and severely degenerative discs was 40%, 37%, 21%, 12%, and 8%, respectively. The decreasing order of the range of motion was young healthy spine > suboptimal healthy spine > mildly degenerative spine > moderately degenerative spine > severely degenerative spine. The mean stress of annulus ground substance in the suboptimal healthy disc was higher than in the young healthy disc. The mean stress of inter-lamellar matrix and annulus ground substance in moderately and severely degenerative discs was higher than in other discs. Age-related structural changes and degenerative changes increased the stiffness and reduced the elastic deformation of intervertebral discs. Decreased range of motion due to the effects of aging or degeneration on the intervertebral disc, may cause compensation of adjacent segments and lead to progressive degeneration of multiple segments. The effect of aging on the intervertebral disc increased the risk of annulus fibrosus damage from the biomechanical point of view. Moderately and severely degenerative discs may have a higher risk of herniation due to the higher risk of damage and layers separation of annulus fibrosus caused by increased stress in the annulus ground substance and inter-lamellar matrix. [ABSTRACT FROM AUTHOR]

• Deep-learning assisted, atlas-based registration methods for skeleton segmentation in whole-body diffusion weighted imaging (WBDWI) can automate disease delineation of bony metastases. • Automated segmentation provides two potential biomarkers of response: median Apparent Diffusion Coefficient (ADC) and total Diffusion Volume (tDV). • The method is evaluated on a multi-center, dataset of patients diagnosed with metastatic prostate cancer or multiple myeloma. • Automated slice detection and spinal segmentation using deep-learning improves the performance of conventional atlas-based registration techniques in WBDWI. Whole-Body Diffusion Weighted MRI (WBDWI) can provide a basis for quantifying disease volume and apparent diffusion coefficient (ADC) to aid disease staging and response assessment of metastatic bone disease. In this study, we develop atlas-based registration assisted by two AI-based models (i) body-region classification for automatic identification of legs, pelvis, lumbar/thoracic/cervical spine and head, and (ii) automated spinal cord segmentation, to automatically delineate the whole skeleton on WBDWI. Training and validation of AI-based models were performed on 40 patients (32:8 split) with confirmed Advanced Prostate Cancer (APC) who underwent baseline and after treatment WBDWI scans. Consequent atlas-based skeleton segmentation accuracy was validated using leave-one-out cross-validation in a cohort of 15 patients with Multiple Myeloma (MM). Atlas-based registration in combination with AI-based models demonstrated higher performance in segmenting the spinal column when compared with the traditional techniques (average dice score for the lumbar, thoracic and cervical spine of 0.77/0.78/0.68 compared to 0.71/0.69/0.51 from the conventional method, respectively). Similar performances were observed for long bones, pelvic girdle and ribcage (average dice score of 0.65/0.7/0.47, respectively). Our approach demonstrated lower relative median ADC and skeleton volume difference between estimation and ground truth compared to conventional atlas-based methods (average relative median ADC and skeleton volume difference of 10.3/2.1% compared to 13/2.7%, respectively). AI-augmented atlas-based registration substantially improves the accuracy of derived skeletal segmentations from WBDWI compared with traditional atlas-based registration. [ABSTRACT FROM AUTHOR]