Crowd-Algorithm Collaboration for Large-Scale Endoscopic Image Annotation with Confidence

Abstract

With the recent breakthrough success of machine learning based solutions for automatic image annotation, the availability of reference image annotations for algorithm training is one of the major bottlenecks in medical image segmentation and many other fields. Crowdsourcing has evolved as a valuable option for annotating large amounts of data while sparing the resources of experts, yet, segmentation of objects from scratch is relatively time-consuming and typically requires an initialization of the contour. The purpose of this paper is to investigate whether the concept of crowd-algorithm collaboration can be used to simultaneously (1) speed up crowd annotation and (2) improve algorithm performance based on the feedback of the crowd. Our contribution in this context is two-fold: Using benchmarking data from the MICCAI 2015 endoscopic vision challenge we show that atlas forests extended by a novel superpixel-based confidence measure are well-suited for medical instrument segmentation in laparoscopic video data. We further demonstrate that the new algorithm and the crowd can mutually benefit from each other in a collaborative annotation process. Our method can be adapted to various applications and thus holds high potential to be used for large-scale low-cost data annotation.