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Point2Mask: A Weakly Supervised Approach for Cell Segmentation Using Point Annotation
German Research Center for Artificial Intelligence (DFKI) GmbH, Kaiserslautern, Germany.
Technische Universität Kaiserslautern, Kaiserslautern, Germany.
German Research Center for Artificial Intelligence (DFKI) GmbH, Kaiserslautern, Germany.
German Research Center for Artificial Intelligence (DFKI) GmbH, Kaiserslautern, Germany.
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2022 (English)In: Medical image understanding and analysis: 26th annual conference, MIUA 2022, Cambridge, UK, July 27–29, 2022, proceedings / [ed] Guang Yang; Angelica Aviles-Rivero; Michael Roberts; Carola-Bibiane Schönlieb, Springer, 2022, p. 139-153Conference paper, Published paper (Refereed)
Abstract [en]

Identifying cells in microscopic images is a crucial step toward studying image-based cell biology research. Cell instance segmentation provides an opportunity to study the shape, structure, form, and size of cells. Deep learning approaches for cell instance segmentation rely on the instance segmentation mask for each cell, which is a labor-intensive and expensive task. An ample amount of unlabeled microscopic data is available in the cell biology domain, but due to the tedious and exorbitant nature of the annotations needed for the cell instance segmentation approaches, the full potential of the data is not explored. This paper presents a weakly supervised approach, which can perform cell instance segmentation by using only point and bounding box-based annotation. This enormously reduces the annotation efforts. The proposed approach is evaluated on a benchmark dataset i.e., LIVECell, whereby only using a bounding box and randomly generated points on each cell, it achieved the mean average precision score of 43.53% which is as good as the full supervised segmentation method trained with complete segmentation mask. In addition, it is 3.71 times faster to annotate with a bounding box and point in comparison to full mask annotation.

Place, publisher, year, edition, pages
Springer, 2022. p. 139-153
Series
Lecture Notes in Computer Science, ISSN 0302-9743, E-ISSN 1611-3349 ; 13413
Keywords [en]
Cell segmentation, Deep learning, Point annotation, Weakly supervised
National Category
Computer Sciences
Identifiers
URN: urn:nbn:se:umu:diva-198916DOI: 10.1007/978-3-031-12053-4_11ISI: 000883331000011Scopus ID: 2-s2.0-85135942969ISBN: 978-3-031-12052-7 (print)ISBN: 978-3-031-12053-4 (electronic)OAI: oai:DiVA.org:umu-198916DiVA, id: diva2:1696642
Conference
26th Annual Conference on Medical Image Understanding and Analysis, MIUA 2022, Cambridge, July 27-29, 2022.
Available from: 2022-09-19 Created: 2022-09-19 Last updated: 2023-09-05Bibliographically approved

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Trygg, JohanSjögren, Rickard

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