Detailed Information on Publication Record

MELNIKOVA, Aleksandra and Petr MATULA. Topology Preserving Segmentation Fusion for Cells with Complex Shapes. Online. In The IEEE International Symposium on Biomedical Imaging. Nice: IEEE, 2021, p. 204-207. ISBN 978-1-6654-1246-9. Available from: https://dx.doi.org/10.1109/ISBI48211.2021.9433867.

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Basic information
Original name	Topology Preserving Segmentation Fusion for Cells with Complex Shapes
Authors	MELNIKOVA, Aleksandra (643 Russian Federation, belonging to the institution) and Petr MATULA (203 Czech Republic, guarantor, belonging to the institution).
Edition	Nice, The IEEE International Symposium on Biomedical Imaging, p. 204-207, 4 pp. 2021.
Publisher	IEEE

Other information
Original language	English
Type of outcome	Proceedings paper
Field of Study	10200 1.2 Computer and information sciences
Confidentiality degree	is not subject to a state or trade secret
Publication form	electronic version available online
RIV identification code	RIV/00216224:14330/21:00118940
Organization unit	Faculty of Informatics
ISBN	978-1-6654-1246-9
ISSN	1945-7928
Doi	http://dx.doi.org/10.1109/ISBI48211.2021.9433867
UT WoS	000786144100046
Keywords in English	Segmentation fusion; Reference annotation; Cell annotation; Shape
Tags	cbia-web, firank_B
Tags	International impact, Reviewed
Changed by	Changed by: Mgr. Michal Petr, učo 65024. Changed: 16/5/2022 14:56.

Abstract
We present an algorithm to fuse simply connected segmentation masks of complex and variable shapes that often appear in cell imaging. The algorithm is designed to preserve topology of the input masks and to faithfully represent their protrusions. It works in three main phases: (1) the detection of geodesic ends that correspond to the protrusions, (2) optimal matching of the geodesic ends, and (3) contour averaging of corresponding boundary segments. We show that our algorithm overcomes commonly used pixel-wise fusion algorithms (namely majority voting, SIMPLE, STAPLE, and topology-preserving STAPLE), as well as recently published geometric median shapes in terms of the visual quality of results as well as better representation of protrusions. We demonstrate the performance of our method based on synthetic images as well as real images from the cell segmentation benchmark datasets.

Abstract

We present an algorithm to fuse simply connected segmentation masks of complex and variable shapes that often appear in cell imaging. The algorithm is designed to preserve topology of the input masks and to faithfully represent their protrusions. It works in three main phases: (1) the detection of geodesic ends that correspond to the protrusions, (2) optimal matching of the geodesic ends, and (3) contour averaging of corresponding boundary segments. We show that our algorithm overcomes commonly used pixel-wise fusion algorithms (namely majority voting, SIMPLE, STAPLE, and topology-preserving STAPLE), as well as recently published geometric median shapes in terms of the visual quality of results as well as better representation of protrusions. We demonstrate the performance of our method based on synthetic images as well as real images from the cell segmentation benchmark datasets.

Links
GA21-20374S, research and development project	Name: Segmentace a sledování buněk se složitým tvarem
GA21-20374S, research and development project	Investor: Czech Science Foundation
MUNI/A/1108/2020, interní kód MU	Name: Rozsáhlé výpočetní systémy: modely, aplikace a verifikace X. (Acronym: SV-FI MAV X.)
MUNI/A/1108/2020, interní kód MU	Investor: Masaryk University