Review of Deep Learning Applications in Spinal Image Segmentation
Deep learning algorithms have the advantages of strong learning,strong adaptive,and unique nonlinear mapping abilities in spinal image segmentation.Compared with traditional segmentation methods,they can better extract key information from spinal images and suppress irrelevant information,which can assist doctors in accurately locating focal areas and realizing accurate and efficient segmentation.The application status of deep learning in spinal image segmentation is summarized and analyzed as concerns deep learning algorithms,types of spinal diseases,types of images,experimental segmentation results,and performance evaluation indicators.First,the background of the deep learning model and spinal image segmentation is described,and thereafter,the application of deep learning in spinal image segmentation is introduced.Second,several common types of spinal diseases are introduced,the difficulties in image segmentation are described,and common open datasets,image segmentation method flow,and image segmentation evaluation indicators are introduced in spinal image segmentation.Combined with specific experiments,the application progress of the Convolutional Neural Network(CNN)model,the U-Net model,and their improved models in the image segmentation of vertebrae,intervertebral discs,and spinal tumors are summarized and analyzed.Combined with previous experimental results and the current research progress of deep learning models,this paper summarizes the limitations of current clinical studies and the reasons for the insufficient segmentation effect,and proposes corresponding solutions to the existing problems.Finally,prospects for future studies and development are proposed.
deep learningConvolutional Neural Network(CNN)U-Netspinal diseasesimage segmentation
国家科技期刊平台
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