A Positive/Unlabeled Approach for the Segmentation of Medical Sequences using Point-Wise Supervision

Lejeune, Laurent; Sznitman, Raphael (2021). A Positive/Unlabeled Approach for the Segmentation of Medical Sequences using Point-Wise Supervision. Medical image analysis, 73, p. 102185. Elsevier 10.1016/j.media.2021.102185

Preview	Text 1-s2.0-S1361841521002310-main.pdf - Accepted Version Available under License Creative Commons: Attribution-Noncommercial-No Derivative Works (CC-BY-NC-ND). Download (3MB) | Preview
	Text 1-s2.0-S1361841521002310-main.pdf - Published Version Restricted to registered users only Available under License Publisher holds Copyright. Download (2MB)

The ability to quickly annotate medical imaging data plays a critical role in training deep learning frameworks for segmentation. Doing so for image volumes or video sequences is even more pressing as annotating these is particularly burdensome. To alleviate this problem, this work proposes a new method to efficiently segment medical imaging volumes or videos using point-wise annotations only. This allows annotations to be collected extremely quickly and remains applicable to numerous segmentation tasks. Our approach trains a deep learning model using an appropriate Positive/Unlabeled objective function using sparse point-wise annotations. While most methods of this kind assume that the proportion of positive samples in the data is known a-priori, we introduce a novel self-supervised method to estimate this prior efficiently by combining a Bayesian estimation framework and new stopping criteria. Our method iteratively estimates appropriate class priors and yields high segmentation quality for a variety of object types and imaging modalities. In addition, by leveraging a spatio-temporal tracking framework, we regularize our predictions by leveraging the complete data volume. We show experimentally that our approach outperforms state-of-the-art methods tailored to the same problem.

Interest & Impact

Downloads

51 since deposited on 11 Aug 2021
33 in the past 12 months

Citations

5 Citations in Web of Science ®
6 Citations in Scopus

Search

in Google Scholar™

Services

Actions (login required)

Edit item

Actions (login required)

Edit item