Fusion of colour contrasted images for early detection of oesophageal squamous cell dysplasia from endoscopic videos in real time

Standard white light (WL) endoscopy often misses precancerous oesophageal changes due to their only subtle differences to the surrounding normal mucosa. While deep learning (DL) based decision support systems benefit to a large extent, they face two challenges, which are limited annotated data sets and insufficient generalisation. This paper aims to fuse a DL system with human perception by exploiting computational enhancement of colour contrast. Instead of employing conventional data augmentation techniques by alternating RGB values of an image, this study employs a human colour appearance model, CIECAM, to enhance the colours of an image. When testing on a frame of endoscopic videos, the developed system firstly generates its contrast-enhanced image, then processes both original and enhanced images one after another to create initial segmentation masks. Finally, fusion takes place on the assembled list of masks obtained from both images to determine the finishing bounding boxes, segments and class labels that are rendered on the original video frame, through the application of non-maxima suppression technique (NMS). This deep learning system is built upon real-time instance segmentation network Yolact. In comparison with the same system without fusion, the sensitivity and specificity for detecting early stage of oesophagus cancer, i.e. low-grade dysplasia (LGD) increased from 75% and 88% to 83% and 97%, respectively. The video processing/play back speed is 33.46 frames per second. The main contribution includes alleviation of data source dependency of existing deep learning systems and the fusion of human perception for data augmentation.