Segmentation of overlapping macrophages using Anglegram analysis

José Alonso Solís-Lemus; Brian Stramer; Greg Slabaugh; Constantino Carlos Reyes-Aldasoro

doi:10.1007/978-3-319-60964-5_69

Segmentation of overlapping macrophages using Anglegram analysis

José Alonso Solís-Lemus^*, Brian Stramer, Greg Slabaugh, Constantino Carlos Reyes-Aldasoro

^*Corresponding author for this work

Queen Mary University of London

Research output: Chapter in Book/Report/Conference proceeding › Other chapter contribution › peer-review

1 Citation (Scopus)

Abstract

This paper describes the automatic segmentation of overlapping cells through different algorithms. As the first step, the algorithm detects junctions between the boundaries of overlapping objects based on the angles between points of the overlapping boundary. For this purpose, a novel 2D matrix with multiscale angle variation is introduced, i.e. anglegram. The anglegram is used to find junctions of overlapping cells. The algorithm to retrieve junctions from the boundary was tested and validated with synthetic data and fluorescently labelled macrophages observed on embryos of Drosophila melanogaster. Then, four different segmentation techniques were evaluated: (i) a Voronoi partition based on the nuclei positions, (ii) a slicing method, which joined the clumps together (junction slicing), (iii) a partition based on the following of the edges from the junctions (edge following), and (iv) a custom self-organising map to fit to the area of overlap between the cells. Only (ii)-(iv) were based on the junctions. The segmentation results were compared based on precision, recall and Jaccard similarity. The algorithm that reported the best segmentation was the junction slicing.

Original language	English
Title of host publication	Medical Image Understanding and Analysis - 21st Annual Conference, MIUA 2017, Proceedings
Publisher	Springer Verlag
Pages	792-803
Number of pages	12
Volume	723
ISBN (Print)	9783319609638
DOIs	https://doi.org/10.1007/978-3-319-60964-5_69
Publication status	E-pub ahead of print - 22 Jun 2017
Event	21st Annual Conference on Medical Image Understanding and Analysis, MIUA 2017 - Edinburgh, United Kingdom Duration: 11 Jul 2017 → 13 Jul 2017

Publication series

Name	Communications in Computer and Information Science
Volume	723
ISSN (Print)	18650929

Conference

Conference	21st Annual Conference on Medical Image Understanding and Analysis, MIUA 2017
Country/Territory	United Kingdom
City	Edinburgh
Period	11/07/2017 → 13/07/2017

Keywords

Macrophages
Overlapping objects
Segmentation
Self-organising maps

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10.1007/978-3-319-60964-5_69

Cite this

Solís-Lemus, J. A., Stramer, B., Slabaugh, G., & Reyes-Aldasoro, C. C. (2017). Segmentation of overlapping macrophages using Anglegram analysis. In Medical Image Understanding and Analysis - 21st Annual Conference, MIUA 2017, Proceedings (Vol. 723, pp. 792-803). (Communications in Computer and Information Science; Vol. 723). Springer Verlag. Advance online publication. https://doi.org/10.1007/978-3-319-60964-5_69

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abstract = "This paper describes the automatic segmentation of overlapping cells through different algorithms. As the first step, the algorithm detects junctions between the boundaries of overlapping objects based on the angles between points of the overlapping boundary. For this purpose, a novel 2D matrix with multiscale angle variation is introduced, i.e. anglegram. The anglegram is used to find junctions of overlapping cells. The algorithm to retrieve junctions from the boundary was tested and validated with synthetic data and fluorescently labelled macrophages observed on embryos of Drosophila melanogaster. Then, four different segmentation techniques were evaluated: (i) a Voronoi partition based on the nuclei positions, (ii) a slicing method, which joined the clumps together (junction slicing), (iii) a partition based on the following of the edges from the junctions (edge following), and (iv) a custom self-organising map to fit to the area of overlap between the cells. Only (ii)-(iv) were based on the junctions. The segmentation results were compared based on precision, recall and Jaccard similarity. The algorithm that reported the best segmentation was the junction slicing.",

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Solís-Lemus, JA, Stramer, B, Slabaugh, G & Reyes-Aldasoro, CC 2017, Segmentation of overlapping macrophages using Anglegram analysis. in Medical Image Understanding and Analysis - 21st Annual Conference, MIUA 2017, Proceedings. vol. 723, Communications in Computer and Information Science, vol. 723, Springer Verlag, pp. 792-803, 21st Annual Conference on Medical Image Understanding and Analysis, MIUA 2017, Edinburgh, United Kingdom, 11/07/2017. https://doi.org/10.1007/978-3-319-60964-5_69

Segmentation of overlapping macrophages using Anglegram analysis. / Solís-Lemus, José Alonso; Stramer, Brian; Slabaugh, Greg et al.
Medical Image Understanding and Analysis - 21st Annual Conference, MIUA 2017, Proceedings. Vol. 723 Springer Verlag, 2017. p. 792-803 (Communications in Computer and Information Science; Vol. 723).

Research output: Chapter in Book/Report/Conference proceeding › Other chapter contribution › peer-review

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AU - Reyes-Aldasoro, Constantino Carlos

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N2 - This paper describes the automatic segmentation of overlapping cells through different algorithms. As the first step, the algorithm detects junctions between the boundaries of overlapping objects based on the angles between points of the overlapping boundary. For this purpose, a novel 2D matrix with multiscale angle variation is introduced, i.e. anglegram. The anglegram is used to find junctions of overlapping cells. The algorithm to retrieve junctions from the boundary was tested and validated with synthetic data and fluorescently labelled macrophages observed on embryos of Drosophila melanogaster. Then, four different segmentation techniques were evaluated: (i) a Voronoi partition based on the nuclei positions, (ii) a slicing method, which joined the clumps together (junction slicing), (iii) a partition based on the following of the edges from the junctions (edge following), and (iv) a custom self-organising map to fit to the area of overlap between the cells. Only (ii)-(iv) were based on the junctions. The segmentation results were compared based on precision, recall and Jaccard similarity. The algorithm that reported the best segmentation was the junction slicing.

AB - This paper describes the automatic segmentation of overlapping cells through different algorithms. As the first step, the algorithm detects junctions between the boundaries of overlapping objects based on the angles between points of the overlapping boundary. For this purpose, a novel 2D matrix with multiscale angle variation is introduced, i.e. anglegram. The anglegram is used to find junctions of overlapping cells. The algorithm to retrieve junctions from the boundary was tested and validated with synthetic data and fluorescently labelled macrophages observed on embryos of Drosophila melanogaster. Then, four different segmentation techniques were evaluated: (i) a Voronoi partition based on the nuclei positions, (ii) a slicing method, which joined the clumps together (junction slicing), (iii) a partition based on the following of the edges from the junctions (edge following), and (iv) a custom self-organising map to fit to the area of overlap between the cells. Only (ii)-(iv) were based on the junctions. The segmentation results were compared based on precision, recall and Jaccard similarity. The algorithm that reported the best segmentation was the junction slicing.

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Y2 - 11 July 2017 through 13 July 2017

ER -

Segmentation of overlapping macrophages using Anglegram analysis

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