The availability of training data for supervision is a frequently encountered bottleneck of medical image analysis methods. While typically established by a clinical expert rater, the increase in acquired imaging data renders traditional pixel-wise segmentations less feasible. In this paper, we examine the use of a crowdsourcing platform for the distribution of super-pixel weak annotation tasks and collect such annotations from a crowd of non-expert raters. The crowd annotations are subsequently used for training a fully convolutional neural network to address the problem of fetal brain segmentation in T2-weighted MR images. Using this approach we report encouraging results compared to highly targeted, fully supervised methods and potentially address a frequent problem impeding image analysis research.
@article{arxiv.1606.01100,
title = {Learning under Distributed Weak Supervision},
author = {Martin Rajchl and Matthew C. H. Lee and Franklin Schrans and Alice Davidson and Jonathan Passerat-Palmbach and Giacomo Tarroni and Amir Alansary and Ozan Oktay and Bernhard Kainz and Daniel Rueckert},
journal= {arXiv preprint arXiv:1606.01100},
year = {2016}
}