Unsupervised Neural Network for Homography Estimation in Capsule Endoscopy Frames

Capsule endoscopy is becoming the major medical technique for the examination of the gastrointestinal tract, and the detection of small bowel lesions. With the growth of endoscopic capsules and the lack of an appropriate tracking system to allow the localization of lesions, the need to develop software-based techniques for the localisation of the capsule at any given frame is also increasing. With this in mind, and knowing the lack of availability of labelled endoscopic datasets, this work aims to develop a unsupervised method for homography estimation in video capsule endoscopy frames, to later be applied in capsule localisation systems. The pipeline is based on an unsupervised convolutional neural network, with a VGG Net architecture, that estimates the homography between two images. The overall error, using a synthetic dataset, was evaluated through the mean average corner error, which was 34 pixels, showing great promise for the real-life application of this technique, although there is still room for the improvement of its performance. (C) 2019 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/) Peer-review under responsibility of the scientific committee of the CENTERIS -International Conference on ENTERprise Information Systems / ProjMAN - International Conference on Project MANagement / HCist - International Conference on Health and Social Care Information Systems and Technologies.