Computer-aided detection (CAD) approaches have shown promising results for early esophageal cancer detection using Volumetric Laser Endoscopy (VLE) imagery. However, the relatively slow and computationally costly tissue segmentation employed in these approaches hamper their clinical applicability. In this paper, we propose to reframe the 2D tissue segmentation problem into a 1D tissue boundary detection problem. Instead of using an encoder-decoder architecture, we propose to follow the tissue boundary using a Recurrent Neural Network (RNN), exploiting the spatio-temporal relations within VLE frames. We demonstrate a near state-of-the-art performance using 18 times less floating point operations, enabling real-time execution in clinical practice.
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.
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