Abstract: CD56 is a nerve cell adhesion molecule that can be used in the diagnosis and study on a variety of tumor cells. CD56 is one of the latest tumor molecular markers, and the research on CD56 images in the field of digital medical image processing has just started. With the development of deep learning technologies such as semantic segmentation, more and more researchers are applying semantic segmentation technology to medical image processing in order to assist doctors in diagnosis. In CD56 images, the proportion of the number of pixel points of background, negative cells and positive cells is very unbalanced, which is roughly 70∶10∶1 and affects the segmentation effect of semantic segmentation technology on CD56 images. In this paper, the loss function of the semantic segmentation model is improved by adding loss weight to different class and adding adaptive weight to each pixel, so that the model can pay more attention to cells, especially positive cells. At the same time, the clustering method is used to refine the annotations of CD56 images before model training, which further improves the segmentation accuracy of the model. The experimental results on CD56 image dataset show that, the refinement of the image annotations and the improvement of the loss functions in the relevant semantic segmentation models can improve the segmentation accuracy of the CD56 images.

Key words: CD56 image, semantic segmentation, deep learning, medical image processing, clustering, adaptive weighted loss