An Unsupervised Approach to Detect Microglia Tip in Volumetric Fluorescence Imaging Data

Mengfan Wang; Kathleen Whiting; Fritz W. Lischka; Zygmunt Galdzicki; Guoqiang Yu

doi:10.1109/ISBI52829.2022.9761491

An Unsupervised Approach to Detect Microglia Tip in Volumetric Fluorescence Imaging Data

Mengfan Wang
, Kathleen Whiting
, Fritz W. Lischka
, Zygmunt Galdzicki
, Guoqiang Yu

Anatomy, Physiology, and Genetics

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

1 Scopus citations

Abstract

Microglia play a key role in maintaining brain health, and the detection of microglia tips is essential for analyzing their motility. However, current tip detection methods either rely on deep neural networks, which require time-consuming annotations, or are unsupervised methods analyzing local patterns, which are significantly influenced by the microglia morphology change. In this paper, we propose an unsupervised, multi-scale microglia tip detection approach. Our approach measures the distance between the candidate tip and the convex hull of adjacent pixels to eliminate the influence of morphology variation. We demonstrated the new approach on volumetric fluorescence imaging data and achieved superior performance compared to peer algorithms.

Original language	English
Title of host publication	IEEE ISBI 2022 Proceedings - 2022 IEEE International Symposium on Biomedical Imaging
Publisher	IEEE Computer Society
ISBN (Electronic)	9781665429238
DOIs	https://doi.org/10.1109/ISBI52829.2022.9761491
State	Published - 2022
Event	19th IEEE International Symposium on Biomedical Imaging, ISBI 2022 - Hybrid, Kolkata, India Duration: 28 Mar 2022 → 31 Mar 2022

Publication series

Name	Proceedings - International Symposium on Biomedical Imaging
Volume	2022-March
ISSN (Print)	1945-7928
ISSN (Electronic)	1945-8452

Conference

Conference	19th IEEE International Symposium on Biomedical Imaging, ISBI 2022
Country/Territory	India
City	Hybrid, Kolkata
Period	28/03/22 → 31/03/22

Keywords

convex hull
fluorescence imaging
Microglia
tip detection
unsupervised learning
volumetric data

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10.1109/ISBI52829.2022.9761491

Cite this

Wang, M., Whiting, K., Lischka, F. W., Galdzicki, Z., & Yu, G. (2022). An Unsupervised Approach to Detect Microglia Tip in Volumetric Fluorescence Imaging Data. In IEEE ISBI 2022 Proceedings - 2022 IEEE International Symposium on Biomedical Imaging (Proceedings - International Symposium on Biomedical Imaging; Vol. 2022-March). IEEE Computer Society. https://doi.org/10.1109/ISBI52829.2022.9761491

@inproceedings{9de7de2160154e7dabe8415f06990046,

title = "An Unsupervised Approach to Detect Microglia Tip in Volumetric Fluorescence Imaging Data",

abstract = "Microglia play a key role in maintaining brain health, and the detection of microglia tips is essential for analyzing their motility. However, current tip detection methods either rely on deep neural networks, which require time-consuming annotations, or are unsupervised methods analyzing local patterns, which are significantly influenced by the microglia morphology change. In this paper, we propose an unsupervised, multi-scale microglia tip detection approach. Our approach measures the distance between the candidate tip and the convex hull of adjacent pixels to eliminate the influence of morphology variation. We demonstrated the new approach on volumetric fluorescence imaging data and achieved superior performance compared to peer algorithms.",

keywords = "convex hull, fluorescence imaging, Microglia, tip detection, unsupervised learning, volumetric data",

author = "Mengfan Wang and Kathleen Whiting and Lischka, \{Fritz W.\} and Zygmunt Galdzicki and Guoqiang Yu",

note = "Publisher Copyright: {\textcopyright} 2022 IEEE.; 19th IEEE International Symposium on Biomedical Imaging, ISBI 2022 ; Conference date: 28-03-2022 Through 31-03-2022",

year = "2022",

doi = "10.1109/ISBI52829.2022.9761491",

language = "English",

series = "Proceedings - International Symposium on Biomedical Imaging",

publisher = "IEEE Computer Society",

booktitle = "IEEE ISBI 2022 Proceedings - 2022 IEEE International Symposium on Biomedical Imaging",

}

Wang, M, Whiting, K, Lischka, FW, Galdzicki, Z & Yu, G 2022, An Unsupervised Approach to Detect Microglia Tip in Volumetric Fluorescence Imaging Data. in IEEE ISBI 2022 Proceedings - 2022 IEEE International Symposium on Biomedical Imaging. Proceedings - International Symposium on Biomedical Imaging, vol. 2022-March, IEEE Computer Society, 19th IEEE International Symposium on Biomedical Imaging, ISBI 2022, Hybrid, Kolkata, India, 28/03/22. https://doi.org/10.1109/ISBI52829.2022.9761491

An Unsupervised Approach to Detect Microglia Tip in Volumetric Fluorescence Imaging Data. / Wang, Mengfan; Whiting, Kathleen; Lischka, Fritz W. et al.
IEEE ISBI 2022 Proceedings - 2022 IEEE International Symposium on Biomedical Imaging. IEEE Computer Society, 2022. (Proceedings - International Symposium on Biomedical Imaging; Vol. 2022-March).

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

TY - GEN

T1 - An Unsupervised Approach to Detect Microglia Tip in Volumetric Fluorescence Imaging Data

AU - Wang, Mengfan

AU - Whiting, Kathleen

AU - Lischka, Fritz W.

AU - Galdzicki, Zygmunt

AU - Yu, Guoqiang

PY - 2022

Y1 - 2022

N2 - Microglia play a key role in maintaining brain health, and the detection of microglia tips is essential for analyzing their motility. However, current tip detection methods either rely on deep neural networks, which require time-consuming annotations, or are unsupervised methods analyzing local patterns, which are significantly influenced by the microglia morphology change. In this paper, we propose an unsupervised, multi-scale microglia tip detection approach. Our approach measures the distance between the candidate tip and the convex hull of adjacent pixels to eliminate the influence of morphology variation. We demonstrated the new approach on volumetric fluorescence imaging data and achieved superior performance compared to peer algorithms.

AB - Microglia play a key role in maintaining brain health, and the detection of microglia tips is essential for analyzing their motility. However, current tip detection methods either rely on deep neural networks, which require time-consuming annotations, or are unsupervised methods analyzing local patterns, which are significantly influenced by the microglia morphology change. In this paper, we propose an unsupervised, multi-scale microglia tip detection approach. Our approach measures the distance between the candidate tip and the convex hull of adjacent pixels to eliminate the influence of morphology variation. We demonstrated the new approach on volumetric fluorescence imaging data and achieved superior performance compared to peer algorithms.

KW - convex hull

KW - fluorescence imaging

KW - Microglia

KW - tip detection

KW - unsupervised learning

KW - volumetric data

UR - https://www.scopus.com/pages/publications/85129654488

U2 - 10.1109/ISBI52829.2022.9761491

DO - 10.1109/ISBI52829.2022.9761491

M3 - Conference contribution

AN - SCOPUS:85129654488

T3 - Proceedings - International Symposium on Biomedical Imaging

BT - IEEE ISBI 2022 Proceedings - 2022 IEEE International Symposium on Biomedical Imaging

PB - IEEE Computer Society

T2 - 19th IEEE International Symposium on Biomedical Imaging, ISBI 2022

Y2 - 28 March 2022 through 31 March 2022

ER -

An Unsupervised Approach to Detect Microglia Tip in Volumetric Fluorescence Imaging Data

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