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

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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.

Item Type:

Journal Article (Original Article)

Division/Institute:

10 Strategic Research Centers > ARTORG Center for Biomedical Engineering Research > ARTORG Center - AI in Medical Imaging Laboratory

Graduate School:

Graduate School for Cellular and Biomedical Sciences (GCB)

UniBE Contributor:

Lejeune, Laurent Georges Pascal, Sznitman, Raphael

Subjects:

000 Computer science, knowledge & systems
600 Technology > 610 Medicine & health
600 Technology > 620 Engineering

ISSN:

1361-8415

Publisher:

Elsevier

Funders:

[4] Swiss National Science Foundation

Language:

English

Submitter:

Raphael Sznitman

Date Deposited:

11 Aug 2021 11:40

Last Modified:

02 Apr 2023 00:25

Publisher DOI:

10.1016/j.media.2021.102185

BORIS DOI:

10.48350/157958

URI:

https://boris.unibe.ch/id/eprint/157958

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