Article Details

Abstract

Medical images are important references for clinical diagnosis. How to segment the lesion areas in medical images quickly and accurately has received extensive attention. At present, the use of Deep Learning for image processing has become the mainstream. Medical image segmentation has become a successful example of Deep Learning in the field of image processing due to its unique application scenarios. With its unique U-shaped structure, the U-Net network has achieved good performance in the field of medical image segmentation, but the network still has problems such as insufficient accuracy. This paper studies the automatic segmentation method of medical images based on the optimized U-Net model. The CBAM and SE modules are combined with the U-Net network structure to achieve highly accurate segmentation of human organs. The experimental results on the eyeball dataset show that the optimized U-Net network has higher accuracy (0.905) than the simple U-Net network. This study has important clinical application prospects, which can effectively segment human organs, lesion areas and other targets, and has a positive impact on medical practice.

Keywords

U-Net neural network; image segmentation; medical image; Attention Mechanism

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About the authors

Zhang Xiaoxu (January 2003-), female, Han ethnicity, from Zhoukou, Henan Province, undergraduate studies, research direction: computer vision; Corresponding author: Wang Jianqing (1975.12-), male, Han ethnicity, from Tangshan, Hebei Province, associate professor, doctoral student, research direction: computer vision, pattern recognition, medical image analysis and processing.