A method based on pix2pix to attenuate bias in the analysis of wound healing assays
DOI:
https://doi.org/10.33448/rsd-v11i12.34271Keywords:
Machine learning; Cell migration; Automated analysis; CGAN.Abstract
The advances of new technologies in the machine learning area have led to the development of conditional generative adversarial networks with the direct use of images, such as is the case of the pix2pix model. A potential application for the pix2pix model discussed in this work is the analysis of images of wound healing or scratch assays that are widely used to evaluate in vitro cell migration. The most common way to evaluate the results of the wound healing assay is by manually detecting the wound area in the image, separating the empty area and the area occupied by cells, during 24, 48 or even 72 h. Although this procedure has for long been presented in the literature, it has been indicated that it lacks objectivity, it is time-consuming, and it leads to data misinterpretation. In an attempt to overcome the lack of robustness and consistency showed by the manual evaluation, this work aims to implement a method based on pix2pix to reduce bias in wound healing analysis, while introducing a new point of view of the images analysis. Manually introduced bias in the image processing algorithm presented deviations of up to 15 % when slightly varying a single variable, while the image processing performed by the model resulted in deviations mostly within 6 % when compared with manual analysis.
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Copyright (c) 2022 Elberth Manfron Schiefer; Andressa Flores Santos; Regiane Stafim da Cunha; Marcia Muller; Andréa Emilia Marques Stinghen; José Luis Fabris; Lucas Hermann Negri
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