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Research on Segmentation and Recognition of Liver CT Image Based on Multi-scale Feature Fusion

Liver segmentation is a common difficulty and key problem in the field of medical imaging. Aiming at the problem that the existing liver CT images have various shapes and the boundary regions are difficult to segment. So an improved U-Net liver segmentation method is proposed. First, introduce the CSPP module with a dilated convolution and multi-scale feature fusion structure to expand the receptive field while extracting richer spatial information; secondly, connect the CSPP modules in series and combine the residual structure to form the DREG module and join the U-Net network jump connection structure. The high-level and low-level feature information is fused to retain the subtle edge information of the liver; the above method solves the problem of diverse sample data shapes and difficult segmentation of boundary regions. Experimental results on the MICCAI 2017 Liver Tumor Segmentation(LiTS) challenge dataset show that this method obtains a good accuracy rate and has high application value for the clinical auxiliary diagnosis of liver CT images.