69 research outputs found
Phenotype-preserving metric design for high-content image reconstruction by generative inpainting
In the past decades, automated high-content microscopy demonstrated its
ability to deliver large quantities of image-based data powering the
versatility of phenotypic drug screening and systems biology applications.
However, as the sizes of image-based datasets grew, it became infeasible for
humans to control, avoid and overcome the presence of imaging and sample
preparation artefacts in the images. While novel techniques like machine
learning and deep learning may address these shortcomings through generative
image inpainting, when applied to sensitive research data this may come at the
cost of undesired image manipulation. Undesired manipulation may be caused by
phenomena such as neural hallucinations, to which some artificial neural
networks are prone. To address this, here we evaluate the state-of-the-art
inpainting methods for image restoration in a high-content fluorescence
microscopy dataset of cultured cells with labelled nuclei. We show that
architectures like DeepFill V2 and Edge Connect can faithfully restore
microscopy images upon fine-tuning with relatively little data. Our results
demonstrate that the area of the region to be restored is of higher importance
than shape. Furthermore, to control for the quality of restoration, we propose
a novel phenotype-preserving metric design strategy. In this strategy, the size
and count of the restored biological phenotypes like cell nuclei are quantified
to penalise undesirable manipulation. We argue that the design principles of
our approach may also generalise to other applications.Comment: 8 pages, 3 figures, conference proceeding
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