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Open AccessArticle

ACDC: Automated Cell Detection and Counting for Time-Lapse Fluorescence Microscopy

1
Department of Radiology, University of Cambridge, Cambridge CB2 0QQ, UK
2
Cancer Research UK Cambridge Centre, University of Cambridge, Cambridge CB2 0RE, UK
3
Department of Haematology, University of Cambridge, Cambridge CB2 0XY, UK
4
Wellcome Trust Sanger Institute, Wellcome Trust Genome Campus, Hinxton CB10 1HH, UK
5
Wellcome Trust—Medical Research Council Cambridge, Stem Cell Institute, Cambridge CB2 0AW, UK
6
Department of Biochemistry, Vanderbilt University School of Medicine, Nashville, TN 37232, USA
7
Department of Informatics, Systems and Communication, University of Milano-Bicocca, 20126 Milan, Italy
8
Institute of Molecular Bioimaging and Physiology, Italian National Research Council, 90015 Cefalù (PA), Italy
9
Department of Industrial Engineering & Innovation Sciences, Eindhoven University of Technology, 5612 AZ Eindhoven, The Netherlands
10
SYSBIO/ISBE.IT Centre for Systems Biology, 20126 Milan, Italy
11
Department of Human and Social Sciences, University of Bergamo, 24129 Bergamo, Italy
*
Authors to whom correspondence should be addressed.
These authors contributed equally to this work.
Co-corresponding authors.
Appl. Sci. 2020, 10(18), 6187; https://doi.org/10.3390/app10186187
Received: 14 July 2020 / Revised: 27 August 2020 / Accepted: 1 September 2020 / Published: 6 September 2020
(This article belongs to the Special Issue Advanced Image Analysis and Processing for Biomedical Applications)
Advances in microscopy imaging technologies have enabled the visualization of live-cell dynamic processes using time-lapse microscopy imaging. However, modern methods exhibit several limitations related to the training phases and to time constraints, hindering their application in the laboratory practice. In this work, we present a novel method, named Automated Cell Detection and Counting (ACDC), designed for activity detection of fluorescent labeled cell nuclei in time-lapse microscopy. ACDC overcomes the limitations of the literature methods, by first applying bilateral filtering on the original image to smooth the input cell images while preserving edge sharpness, and then by exploiting the watershed transform and morphological filtering. Moreover, ACDC represents a feasible solution for the laboratory practice, as it can leverage multi-core architectures in computer clusters to efficiently handle large-scale imaging datasets. Indeed, our Parent-Workers implementation of ACDC allows to obtain up to a 3.7× speed-up compared to the sequential counterpart. ACDC was tested on two distinct cell imaging datasets to assess its accuracy and effectiveness on images with different characteristics. We achieved an accurate cell-count and nuclei segmentation without relying on large-scale annotated datasets, a result confirmed by the average Dice Similarity Coefficients of 76.84 and 88.64 and the Pearson coefficients of 0.99 and 0.96, calculated against the manual cell counting, on the two tested datasets. View Full-Text
Keywords: bioimage informatics; time-lapse microscopy; fluorescence imaging; cell counting; nuclei segmentation bioimage informatics; time-lapse microscopy; fluorescence imaging; cell counting; nuclei segmentation
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MDPI and ACS Style

Rundo, L.; Tangherloni, A.; Tyson, D.R.; Betta, R.; Militello, C.; Spolaor, S.; Nobile, M.S.; Besozzi, D.; Lubbock, A.L.R.; Quaranta, V.; Mauri, G.; Lopez, C.F.; Cazzaniga, P. ACDC: Automated Cell Detection and Counting for Time-Lapse Fluorescence Microscopy. Appl. Sci. 2020, 10, 6187.

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