A Web-Based Platform for Distributed Annotation of Computerized Tomography Scans

Computer Aided Diagnosis (CAD) systems are adopting advancements at the forefront of computer vision and machine learning towards assisting medical experts with providing faster diagnoses. The success of CAD systems heavily relies on the availability of high-quality annotated data. Towards supporting the annotation process among teams of medical experts, we present a web-based platform developed for distributed annotation of medical images. We capitalize on the HTML5 canvas to allow for medical experts to quickly perform segmentation of regions of interest. Experimental evaluation of the proposed platform show a significant reduction in the time required to perform the annotation of abdominal computerized tomography images. Furthermore, we evaluate the relationship between the size of the harvested regions and the quality of the annotations. Finally, we present additional functionality of the developed platform for the closer examination of 3D point clouds for kidney cancer.