Kvasir-Capsule, a video capsule endoscopy dataset.

Artificial intelligence (AI) is predicted to have profound effects on the future of video capsule endoscopy (VCE) technology. The potential lies in improving anomaly detection while reducing manual labour. Existing work demonstrates the promising benefits of AI-based computer-assisted diagnosis systems for VCE. They also show great potential for improvements to achieve even better results. Also, medical data is often sparse and unavailable to the research community, and qualified medical personnel rarely have time for the tedious labelling work. We present Kvasir-Capsule, a large VCE dataset collected from examinations at a Norwegian Hospital. Kvasir-Capsule consists of 117 videos which can be used to extract a total of 4,741,504 image frames. We have labelled and medically verified 47,238 frames with a bounding box around findings from 14 different classes. In addition to these labelled images, there are 4,694,266 unlabelled frames included in the dataset. The Kvasir-Capsule dataset can play a valuable role in developing better algorithms in order to reach true potential of VCE technology.

Computer-assisted laser photocoagulation of the retina--a hybrid tracking approach.

A system for robotically assisted retinal surgery has been developed to rapidly and safely place lesions on the retina for photocoagulation therapy. This system provides real-time, motion stabilized lesion placement for typical irradiation times of 100 ms. The system consists of three main subsystems: a digital-based global tracking subsystem; a fast, analog local tracking subsystem; and a confocal reflectance subsystem to control lesion parameters dynamically. We have reported previously on these individual subsystems. This paper concentrates on the development of a second hybrid system prototype. Considerable progress has been made toward reducing the footprint of the optical system, simplifying the user interface, fully characterizing the analog tracking system, using measurable lesion reflectance parameters to develop a noninvasive method to infer lesion depth, and integrating the subsystems into a seamless hybrid system. These system improvements and progress toward a clinically significant system are covered in detail within this paper. The tracking algorithms and concepts developed for this project have considerable potential for application in many other areas of biomedical engineering.