Comprehensive Analysis of Deep Learning in Spine Image Segmentation

A clinical diagnosis and treatment plan for spinal disorders must start with an imaging assessment. The complicated structure of the spine is encircled by nerve tissue, blood vessels, and muscles. Spine morphology can be easily altered by pathological alterations. Precise division of spine images can be useful. Physicians are able to precisely identify lesions, assess lesions quickly, and direct surgical intervention. Because of its strong feature learning capabilities, deep learning has been applied extensively in the field of spine segmentation in recent years. We compare the traditional deep learning frameworks, provide the widely used data sets and evaluation indicators in spine image segmentation, and provide an overview of CNN, FCN, U-Net, and GAN in order to examine the state of research and development of deep learning in spine image segmentation tasks. The most recent developments in the use of various network models for spine segmentation are presented, along with an analysis of the models' characteristics, a detailed discussion of the difficulties and issues that deep learning is currently facing, and a prospect for future development.