Thymic cell image segmentation method based on improved U-Net network

Abstract

The present invention discloses a thymic cell image segmentation method based on an improved U-Net network, which comprises the following steps: carrying out image preprocessing on a UCSB breast imagedata set; in a U-Net network, adding a void residual module and an attention module; training the U-NET network according to the set training strategy; setting up the evaluation indexes including F1score, object-level Dice coefficient and Hausdorff distance. The optimal model is obtained by optimizing the network through the evaluation indexes. The cell image to be segmented is inputted into theoptimal model, and the segmentation mask is obtained by feature extraction and feature upsampling. By improving a basic segmentation network and creating a new cell image segmentation method, the invention solves the problem of low precision in the automatic segmentation process of the thymus image, and improves the accuracy and efficiency of the segmentation.

Description

technical field [0001] The invention belongs to the field of image processing and relates to a thymocyte image segmentation method based on an improved U-Net network. Background technique [0002] In recent years, the incidence of colon cancer has increased. Colorectal cancer is the third most common cancer in men and the second most common cancer in women. Approximately 95% of colorectal cancers are adenocarcinomas. Typically, a typical gland consists of a luminal region forming an inner tubular structure and an epithelial nucleus surrounding the cytoplasm. Malignancies arising from the glandular epithelium, also known as adenocarcinomas, are the most prevalent form of cancer. On histopathological examination, glandular morphology is widely used to evaluate several adenocarcinomas, including breast, prostate, and colon. Accurate gland segmentation is a key prerequisite for obtaining reliable morphological statistics that indicate tumor aggressiveness. Previously, gland s...

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Problems solved by technology

[0005] In order to solve the above problems, the present invention creates a new cell image segmentation method by improving a basic segmentation network, which solves the problem of low precision in the process of automatic segmentation of thymus images, and improves the accuracy and efficiency of segmentation

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