Analysis And Prognosis Of Colorectal Cancer Immunohistochemical Pathology Images Based On Deep Learning

Colorectal cancer is the third most common cancer globally and the second leading cause of cancer-related deaths.Exploring novel biomarkers in the tumor microenvironment of colorectal cancer pathology images has significant clinical value for prognosis.With the advancement of artificial intelligence technology,using deep learning to assist digital pathology image analysis and in-depth exploration of biomarkers has become a research hotspot.This study mainly focuses on the following three issues:(1)Digital pathology images have high-resolution and large-scale features,and relying solely on manual outlining to study the tumor microenvironment is time-consuming and labor-intensive;(2)The annotation process for pathology images is time-consuming and requires a certain level of pathology knowledge and experience.Deep learning needs high-quality data,and there is currently a lack of unified standards to assess the quality of digital pathology images,with significant differences between images produced by different hospitals and equipment;(3)The complexity of the colorectal cancer tumor microenvironment makes the relationships between cells intricate and difficult to fully understand.In response to the above issues,this study focuses on the automatic tissue segmentation,cell nucleus segmentation,and prognostic study of novel biomarkers in the tumor microenvironment of colorectal cancer immunohistochemical pathology images.The main research content includes:(1)Using the deep learning VGG-19 model to achieve automated coarse tissue segmentation,obtaining the tumor region;(2)Proposing an unsupervised algorithm to pre-segment the cell nuclei in the tumor area,followed by manual annotation,thereby providing a gold standard for training the deep learning Hover-Net model and significantly reducing the annotation cost.Using deep learning VGG-19 style transfer for data augmentation to improve the model’s generalization ability across different centers;(3)Based on the cell nucleus segmentation results,the Morisita-Horn index(Mor-index)is used to quantify the spatial relationship between immune cells and tumor cells in the tumor microenvironment and to perform clinical prognosis analysis based on this index.