Research on Multi-organ Edge Segmentation Technology in Abdominal MRI Based on Unet++

With the development of medical imaging technology, abdominal MRI has become an important tool for the evaluation and diagnosis of a variety of diseases. However, accurate organ edge segmentation is essential to improve diagnostic accuracy. This study aims to develop an automatic segmentation method based on deep learning to improve the accuracy and efficiency of multi-organ edge segmentation in abdominal MRI. First, we collected a set of abdominal MRI image datasets containing different pathological states and preprocessed them, including denoising, contrast enhancement, and normalization. Then, we designed a deep learning network architecture that combines the characteristics of convolutional neural networks (CNNs) and U-Net++ models to make full use of contextual information in the image and improve the accuracy of edge detection.