<p>This paper suggests an automated system for segmentation of the Lumbo-Sacral (LS) Magnetic Resonance Imaging (MRI) of spine and evaluation of its geometrical characteristics. The LS MRI of spine is segmented into anatomical parts such as vertebrae, intervertebral discs (IVDs), canal and vertebral height and width, IVDs height and width, canal diameter, IVDs height index and signal intensity parameters are computed to facilitate automatic analysis. To overcome the subjectivity and variability that come with manual analysis, an expert-verified dataset is developed. This dataset improves clinical outcomes and diagnostic accuracy by facilitating objective and consistent lumbar spine analysis. Furthermore, the generated data supports the development of personalized treatment programs that enhance patient care. For segmentation of LS spine MRIs, we have utilized DeepLabV3 + with ResNet50 and attention gate. Automated quantitative analysis of LS spine enables several therapeutic advantages such as (1) lessens the workload of the radiologists, allowing them to concentrate on challenging cases and improves productivity during routine evaluations (2) offers objective and consistent analysis and boosts diagnostic precision by minimizing mistakes (3) enables early spine pathology detection and easy monitoring allowing for timely medications. The suggested automating model is highlighted by its capability to improve clinical efficiency, accuracy, patient care quality and will have a potential influence on the management of spinal health and the treatment of low back pain. Index term- Lumbar spine MRI, Segmentation, Back pain, DeepLabV3 + , Personalized treatment.</p>