Using a 22-Layer U-Net to Perform Segmentation of Squamous Cell Carcinoma on Digitized Head and Neck Histological Images

Proc SPIE Int Soc Opt Eng. 2020 Feb;11320:113200C. doi: 10.1117/12.2549061. Epub 2020 Mar 16.

Abstract

Squamous cell carcinoma (SCC) comprises over 90 percent of tumors in the head and neck. The diagnosis process involves performing surgical resection of tissue and creating histological slides from the removed tissue. Pathologists detect SCC in histology slides, and may fail to correctly identify tumor regions within the slides. In this study, a dataset of patches extracted from 200 digitized histological images from 84 head and neck SCC patients was used to train, validate and test the segmentation performance of a fully-convolutional U-Net architecture. The neural network achieved a pixel-level segmentation AUC of 0.89 on the testing group. The average segmentation time for whole slide images was 72 seconds. The training, validation, and testing process in this experiment produces a model that has the potential to help segment SCC images in histological images with improved speed and accuracy compared to the manual segmentation process performed by pathologists.