Area-based cell colony surviving fraction evaluation: A novel fully automatic approach using general-purpose acquisition hardware. (1st October 2017)

• Record Type: Journal Article
• Authors: Militello, Carmelo; Rundo, Leonardo; Conti, Vincenzo; Minafra, Luigi; Cammarata, Francesco Paolo; Mauri, Giancarlo; Gilardi, Maria Carla; Porcino, Nunziatina
• Is Part Of: Computers in biology and medicine. Volume 89(2017)
• Page Start: 454
• Page End: 465
• Publication Date: 2017-10-01
• Subjects: Fully automatic surviving fraction evaluation -- Area covered by colony -- Clonogenic assays -- Circle Hough transform -- Local adaptive thresholding -- General-purpose acquisition hardware; Medicine -- Data processing -- Periodicals; Biology -- Data processing -- Periodicals; 610.285
• DOI: 10.1016/j.compbiomed.2017.08.005
• Languages: English
• ISSNs: 0010-4825

Abstract

Abstract: Background: The current methodology for the Surviving Fraction (SF) measurement in clonogenic assay, which is a technique to study the anti-proliferative effect of treatments on cell cultures, involves manual counting of cell colony forming units. This procedure is operator-dependent and error-prone. Moreover, the identification of the exact colony number is often not feasible due to the high growth rate leading to the adjacent colony merging. As a matter of fact, conventional assessment does not deal with the colony size, which is generally correlated with the delivered radiation dose or the administered cytotoxic agent. Method: Considering that the Area Covered by Colony (ACC) is proportional to the colony number and size as well as to the growth rate, we propose a novel fully automatic approach exploiting Circle Hough Transform, to automatically detect the wells in the plate, and local adaptive thresholding, which calculates the percentage of ACC for the SF quantification. This measurement relies just on this covering percentage and does not consider the colony number, preventing inconsistencies due to intra- and inter-operator variability. Results: To evaluate the accuracy of the proposed approach, we compared the SFs obtained by our automatic ACC-based method against the conventional counting procedure. The achieved results ( r = 0.9791 and r = 0.9682 on MCF7 and MCF10A cells, respectively) showed values highly correlated with the measurements using the traditional approach based on colony number alone. Conclusions: The proposed computer-assisted methodology could be integrated in laboratory practice as an expert system for the SF evaluation in clonogenic assays. Graphical abstract: Overall flow diagram of the processing pipeline of the proposed fully automatic approach. Four main stages can be identified: (i) image-preprocessing, basically dealing with the RGB to CIE L*u*v* conversion; (ii) multi-well detection, which identifies and selects the wells inside the multi-well plate image, by exploiting Circle Hough Transform; (iii) colony extraction, based on a local adaptive thresholding algorithm, which segments cell colonies from the well background; (iv) Surviving Fraction (SF) evaluation, which measures the SF of the current well according to the treated ACCT against the reference untreated ACCU (control). Highlights: Novel fully automatic approach for the Surviving Fraction (SF) evaluation in clonogenic assays. No dedicated hardware devices are required for multi-well plate image acquisition. The SF is measured considering the percentage of Area Covered by Colony (ACC). Circle Hough Transform and local adaptive thresholding techniques are used. The achieved SFs were highly correlated with the conventional counting method.