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A computed tomography image segmentation algorithm for improving the diagnostic accuracy of rectal cancer based on U-net and residual block
Ist Teil von
Sheng wu yi xue gong cheng xue za zhi, 2022-02, Vol.39 (1), p.166
Ort / Verlag
China
Erscheinungsjahr
2022
Link zum Volltext
Beschreibungen/Notizen
As an important basis for lesion determination and diagnosis, medical image segmentation has become one of the most important and hot research fields in the biomedical field, among which medical image segmentation algorithms based on full convolutional neural network and U-Net neural network have attracted more and more attention by researchers. At present, there are few reports on the application of medical image segmentation algorithms in the diagnosis of rectal cancer, and the accuracy of the segmentation results of rectal cancer is not high. In this paper, a convolutional network model of encoding and decoding combined with image clipping and pre-processing is proposed. On the basis of U-Net, this model replaced the traditional convolution block with the residual block, which effectively avoided the problem of gradient disappearance. In addition, the image enlargement method is also used to improve the generalization ability of the model. The test results on the data set provided by the "Teddy Cup" Data M