Fast tissue detection in volumetric laser endomicroscopy using convolutional neural networks: an object-detection approach

Barrett's Esophagus is a precursor of esophageal adenocarcinoma, one of the most lethal forms of cancer. Volumetric laser endomicroscopy (VLE) is a relatively new technology used for early detection of abnormal cells in BE by imaging the inner tissue layers of the esophagus. Computer-Aided Detection (CAD) shows great promise in analyzing the VLE frames due to the advances in deep learning. However, a full VLE scan produces 1,200 scans of 4,096 x 2,048 pixels, making automated pre-processing for the tissue of interest extraction necessary. This paper explores an object detection for tissue detection in VLE scans. We show that this can be achieved in real time with very low inference time, using single-stage object detection like YOLO. Our best performing model achieves a value of 98.23% for the mean average precision of bounding boxes correctly predicting the tissue of interest. Additionally, we have found that the tiny YOLO with Partial Residual Networks architecture further reduces the inference speed with a factor of 10, while only sacrificing less than 1% of accuracy. This proposed method does not only segment the tissue of interest in real time without any latency, but it can also achieve this efficiently using limited GPU resources, rendering it attractive for embedded applications. Our paper is the first to introduce object detection as a new approach for VLE-data tissue segmentation and paves the way for real-time VLE-based detection of early cancer in BE.