Spinal Malalignment is a chronic disease that is widespread across the world. It causes different diseases such as Stenosis, Scoliosis, Osteoporotic Fractures, Thoracolumbar fractures, Disc degeneration, etc. The diagnosis of such disease is generally done by analyzing the Magnetic Resonance Imaging (MRI) scan of the lumbar spine region. MRI analysis is done by well experienced medical professionals (radiologists and orthopedists). The flip side to this inspection is that it is time-consuming and may be subjected to a lack of accuracy. The manual segmentation of MRI scans from a large number of scan images is a tedious and time-consuming process. Thus, there is a need for automatic segmentation and analysis of spine MRI scans to improve clinical outputs and the accuracy of spinal measurements. In recent, the rise of deep learning technologies is making a revolution in medical systems. It is capable of analyzing a large amount of data and yield better accuracy. So, deep learning approaches can be efficiently used for the automatic segmentation of MRI scans. In this paper, an overview of spinal MRI segmentation using deep learning techniques is presented. The disease diagnosis from spine MRI is conferred. Then the state-of-art research in the automatic image segmentation using Convolutional Neural Network (CNN) is discussed. A comparative analysis is done on various deep learning techniques based on the performance metrics is presented. Finally, the evaluation metrics for automatic segmentation is provided along with the comparison of the state-of-art results.