Machine Learning, OCT Combine For Early-Stage Colon Cancer Detection

Researchers at Washington University in St. Louis are developing a deep learning-based pattern recognition (PR)-OCT system that will automate image processing and provide accurate, computer-aided diagnosis of colorectal cancer potentially in real time. The technique combines OCT machine learning and deep learning to detect patterns in the images of normal and abnormal tissue samples.

The researchers began using OCT in 2017 as a research tool to image samples of colorectal tissue removed from patients at the Washington University School of Medicine. Yifeng Zeng, a biomedical engineering doctoral student, observed that the healthy colorectal tissue had a pattern that looked similar to teeth. However, the precancerous and cancerous tissues rarely showed this pattern. The teeth pattern was caused by light attenuation of the healthy mucosa microstructures of the colorectal tissue.

Zeng began working with Shiqi Xu, also a graduate student, to train RetinaNet, a neural network model of the brain, to capture and learn the structural patterns in human colon OCT machine learning images. The researchers trained and tested the network using about 26,000 OCT images acquired from 20 tumor areas, 16 benign areas, and six other abnormal areas in patient tissue samples.

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