Topology Preserving Segmentation Fusion for Cells with Complex
Shapes

D 2021

Topology Preserving Segmentation Fusion for Cells with Complex Shapes

MELNIKOVA, Aleksandra and Petr MATULA

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

Language

English

Type of outcome

Stať ve sborníku

Field of Study

10200 1.2 Computer and information sciences

Confidentiality degree

není předmětem státního či obchodního tajemství

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

DOI

http://dx.doi.org/10.1109/ISBI48211.2021.9433867

UT WoS

000786144100046

Keywords in English

Segmentation fusion; Reference annotation; Cell annotation; Shape

Abstract

V originále

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

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.)

Investor: Masaryk University

Citovat

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.

@inproceedings{1762543,
   author = {Melnikova, Aleksandra and Matula, Petr},
   address = {Nice},
   booktitle = {The IEEE International Symposium on Biomedical Imaging},
   doi = {http://dx.doi.org/10.1109/ISBI48211.2021.9433867},
   keywords = {Segmentation fusion; Reference annotation; Cell annotation; Shape},
   howpublished = {elektronická verze "online"},
   language = {eng},
   location = {Nice},
   isbn = {978-1-6654-1246-9},
   pages = {204-207},
   publisher = {IEEE},
   title = {Topology Preserving Segmentation Fusion for Cells with Complex Shapes},
   year = {2021}
}

TY  - JOUR
ID  - 1762543
AU  - Melnikova, Aleksandra - Matula, Petr
PY  - 2021
TI  - Topology Preserving Segmentation Fusion for Cells with Complex Shapes
PB  - IEEE
CY  - Nice
SN  - 9781665412469
KW  - Segmentation fusion
KW  - Reference annotation
KW  - Cell annotation
KW  - Shape
N2  - 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.
ER  -

MELNIKOVA, Aleksandra and Petr MATULA. Topology Preserving Segmentation Fusion for Cells with Complex Shapes. Online. In \textit{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.

Detailed Information on Publication Record