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

Applied Sciences

卷:10

DarrenR. Tyson¹  CarlosF. Lopez¹  Vito Quaranta¹  AlexanderL. R. Lubbock¹  Andrea Tangherloni²  MarcoS. Nobile³  Simone Spolaor⁴  Daniela Besozzi⁴  Giancarlo Mauri⁴  Riccardo Betta⁴  Leonardo Rundo⁵  Carmelo Militello⁶  Paolo Cazzaniga⁷ 

[1] Department of Biochemistry, Vanderbilt University School of Medicine, Nashville, TN 37232, USA;

[2] Department of Haematology, University of Cambridge, Cambridge CB2 0XY, UK;

[3] Department of Industrial Engineering & Innovation Sciences, Eindhoven University of Technology, 5612 AZ Eindhoven, The Netherlands;

[4] Department of Informatics, Systems and Communication, University of Milano-Bicocca, 20126 Milan, Italy;

[5] Department of Radiology, University of Cambridge, Cambridge CB2 0QQ, UK;

[6] Institute of Molecular Bioimaging and Physiology, Italian National Research Council, 90015 Cefalù (PA), Italy;

[7] SYSBIO/ISBE.IT Centre for Systems Biology, 20126 Milan, Italy;

关键词: bioimage informatics;    time-lapse microscopy;    fluorescence imaging;    cell counting;    nuclei segmentation;   

DOI  :  10.3390/app10186187

来源: DOAJ

【 摘 要 】

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 $3.7\times$ 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.

【 授权许可】

Unknown