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The high cure rate of cancer is inextricably linked to physicians' accuracyin diagnosis and treatment, therefore a model that can accomplishhigh-precision tumor segmentation has become a necessity in many applicationsof the medical industry. It can effectively lower the rate of misdiagnosiswhile considerably lessening the burden on clinicians. However, fully automatedtarget organ segmentation is problematic due to the irregular stereo structureof 3D volume organs. As a basic model for this class of real applications,U-Net excels. It can learn certain global and local features, but still lacksthe capacity to grasp spatial long-range relationships and contextualinformation at multiple scales. This paper proposes a tumor segmentation modelMPU-Net for patient volume CT images, which is inspired by Transformer with aglobal attention mechanism. By combining image serialization with the PositionAttention Module, the model attempts to comprehend deeper contextualdependencies and accomplish precise positioning. Each layer of the decoder isalso equipped with a multi-scale module and a cross-attention mechanism. Thecapability of feature extraction and integration at different levels has beenenhanced, and the hybrid loss function developed in this study can betterexploit high-resolution characteristic information. Moreover, the suggestedarchitecture is tested and evaluated on the Liver Tumor Segmentation Challenge2017 (LiTS 2017) dataset. Compared with the benchmark model U-Net, MPU-Netshows excellent segmentation results. The dice, accuracy, precision,specificity, IOU, and MCC metrics for the best model segmentation results are92.17%, 99.08%, 91.91%, 99.52%, 85.91%, and 91.74%, respectively. Outstandingindicators in various aspects illustrate the exceptional performance of thisframework in automatic medical image segmentation.

3D Medical Image Segmentation based on multi-scale MPU-Net