Image Segmentation Based on Improved Unet

In order to help doctors diagnose and treat liver lesions and accurately segment liver images, this paper proposes an improved Unet network, which adds compression extraction modules and full-scale connection blocks, extracts input image features, and achieves accurate segmentation of liver images. The compression extraction module distributes weights to convolutional layers of different sizes, which is conducive to the extraction of image spatial information and context information. Full-scale blocks are connected by skipping,combining the higher semantic information from the decoder and corresponding the lowwer semantic information from the encoder to strengthen the ability to extract tumor edge information. This article includes 25 cases from the Lits liver dataset. The dataset is classified as the training dataset and the test dataset, and the image blocks are extracted after gray-scale normalization and input to the network to acquire the final segmentation results. The segmentation result is evaluated by F1 score. Comparing multiple sets of experiments, compared with general network structures such as Unet and AttenUnet, it shows that the network architecture proposed in the Dissertation improves the accuracy and efficiency of liver image segmentations.