Semantic segmentation of medical images is a crucial step for the quantification of healthy anatomy and diseases alike. The majority of the current state-of-the-art segmentation algorithms are based on deep neural networks and rely on large datasets with full pixel-wise annotations. Producing such annotations can often only be done by medical professionals and requires large amounts of valuable time. Training a medical image segmentation network with weak annotations remains a relatively unexplored topic. In this work we investigate training strategies to learn the parameters of a pixel-wise segmentation network from scribble annotations alone. We evaluate the techniques on public cardiac (ACDC) and prostate (NCI-ISBI) segmentation datasets. We find that the networks trained on scribbles suffer from a remarkably small degradation in Dice of only 2.9% (cardiac) and 4.5% (prostate) with respect to a network trained on full annotations.
@article{arxiv.1807.04668,
title = {Learning to Segment Medical Images with Scribble-Supervision Alone},
author = {Yigit B. Can and Krishna Chaitanya and Basil Mustafa and Lisa M. Koch and Ender Konukoglu and Christian F. Baumgartner},
journal= {arXiv preprint arXiv:1807.04668},
year = {2018}
}