Medical image segmentation is a key technology in the field of medical image processing and analysis, which aims to segment special parts of medical images and extract relevant features to provide a reliable basis for clinical diagnosis and pathology research, and to assist doctors in making accurate judgments. Accurate nuclear segmentation is the basis of cell detection, cell classification and tumor grading, and has gradually attracted great attention from researchers in recent years. Due to the low contrast of nucleus images, large differences in the spatial distribution of nuclei, and easy formation of adhesions between cells, the accurate segmentation of nucleus images has become one of the difficult problems in medical image analysis. Based on the deep learning image segmentation algorithm and the U-Net network model, this paper designs a nucleus segmentation model based on ResNet residual blocks, and optimizes its network structure to achieve accurate segmentation of nucleus images.

Ruohuan Tang, Zhiyong Liu, Jianqiang Jia. Research on the nucleus segmentation method based on improved U-Net network. Academic Journal of Computing & Information Science (2023), Vol. 6, Issue 11: 88-94. https://doi.org/10.25236/AJCIS.2023.061112.

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