Multi-tissue Partitioning for Whole Slide Images of Colorectal Cancer Histopathology Images with Deeptissue Net

Tissue composition plays an essential role in diagnosis and prognosis of colorectal cancer (CRC). Studies have shown that the relative proportion of tissue composition on colorectal specimens is potentially prognostic of outcome in CRC patients. Some of the important tissue partitions include blood vessel, tumor epithelium, adipose tissue, mucosal glands, mucus, muscle, stroma, necrosis, immune cell, and background/other tissues. A challenge in accurately determining quantitative measurements of tissue composition however is in the need for automated tissue partitioning image analysis tools. Towards this goal, we present a Deeptissue Net, a deep learning strategy which involves integrating DenseNet with Focal Loss. In order to show the effectiveness of Deeptissue Net, the model was trained with 40 WSIs from one site and tested on 620 WSIs from two sites. 10 distinct tissue partitions are blood vessel, tumor epithelium, adipose tissue, mucosal glands, mucus, muscle, stroma, necrosis, immune cell, and background/other tissues. The ground truth for training and evaluating Deeptissue Net involved careful annotation of the different tissue compartments by expert pathologists. The Deeptissue net was trained with the tissue partitions delineated for the 10 classes on the 40 WSIs and subsequently evaluated on the remaining $$N=620$$ datasets. By measuring with confusion matrices, the Deeptissue Net achieves the accuracy of 0.72, 0.84, and 0.88 in classifying mucus, stroma, and necrosis on the 2nd batch of Dataset 1; 0.85 and 0.96 in classifying mucus and muscle on Dataset 2, respectively, which significantly outperformed DenseNet and ResNet.