Spinal magnetic resonance image segmentation based on U-net

With the continuous progress of computer and medical imaging technology, medical image segmentation has gradually become a hot topic in medical image technology research, playing an essential role in the medical field. Magnetic resonance imaging (MRI) can sensitively detect changes in water content in tissue components, display changes in physiological and biochemical information such as function and metabolic processes, and provide the diagnostic basis for some early lesions; it is often more effective and early in detecting lesions than CT, and does not produce ionizing radiation that is harmful to the human body, which is widely used in spinal imaging. MRI analysts (radiologists and orthopedics) can quickly read the lesion site from the presented images. One drawback of this method is that it is time consuming and needs more accurately. Manually segmenting MRI scanned images from many scanned images takes time and effort. Therefore, it is crucial to choose automatic segmentation and analysis of spinal MRI scans to improve the accuracy of clinical diagnosis and greatly assist patients in their treatment. By using appropriate methods of artificial intelligence, we can achieve the localization and segmentation of spinal structures, as well as comprehensive analysis of the diagnosis and differential diagnosis, clinical decision support, and prognosis prediction of spinal diseases, providing a basis for selecting the most reasonable treatment method for spinal diseases. The rise of deep learning technology has brought good news to the medical field. Deep learning is a specific type of machine learning. Technologies in artificial intelligence, and intense learning methods, have been widely applied in medical image and big data processing, including image reconstruction, image processing, image analysis, and model construction. It can quickly analyze a large amount of data and generate good accuracy. Therefore, deep learning methods can be effectively applied to segment MAI images automatically. Based on the characteristics of spinal MRI images and the sharp contrast between the gray levels of intervertebral discs and vertebrae in MRI images, a cross-validation method was used to propose the use of convolutional neural networks for precise segmentation of spinal MRI images, which achieved good results with an average segmentation accuracy of over 88%.