Large-scale tissue histopathology image segmentation based on feature pyramid

Histopathology image analysis is a gold standard for cancer recognition and diagnosis. But typical problems with histopathology images that hamper automatic analysis include complex clinical features, insufficient training data, and large size of a single image (always up to gigapixels). In this paper, an image semantic segmentation algorithm based on feature Pyramid (ResNet50-GICN-GPP) is proposed. Firstly, the patch sampling method is adopted to resample the image, reduce the size of a single sample, and expand the number of training samples. Secondly, design the whole convolution network based on ResNet50 learning feature location information, then use GICN structure and deconvolution network to integrate multi-level features. Finally, in order to solve the problem that the GICN structure may lose the small object, the GPP structure should be joined to explore the multi-scale semantic information. The proposed method achieves 63% of the average segmentation accuracy (Dice coefficient) on Camelyon16 and Gastric WSIs Data, compared with U-Net, FCN and SegNet which has 10~20% improvement, and fully demonstrates the effectiveness of this method in different types of cancer. By experimentally comparing the segmentation accuracy of various scales of cancerous tissues, the performance of ResNet50-GICN-GPP is balanced and the multi-scale information localization is more accurate.