DeadNet: Identifying Phototoxicity from Label-free Microscopy Images of Cells using Deep ConvNets
This addresses the issue of unaccounted phototoxicity effects in imaging experiments for biologists, though it is incremental as it builds on existing deep learning methods.
The paper tackled the problem of identifying phototoxicity in live-cell fluorescence microscopy without additional labels by using deep convolutional neural networks on single phase-contrast images, achieving a proof-of-principle demonstration with potential for automated cell health assessment.
Exposure to intense illumination light is an unavoidable consequence of fluorescence microscopy, and poses a risk to the health of the sample in every live-cell fluorescence microscopy experiment. Furthermore, the possible side-effects of phototoxicity on the scientific conclusions that are drawn from an imaging experiment are often unaccounted for. Previously, controlling for phototoxicity in imaging experiments required additional labels and experiments, limiting its widespread application. Here we provide a proof-of-principle demonstration that the phototoxic effects of an imaging experiment can be identified directly from a single phase-contrast image using deep convolutional neural networks (ConvNets). This lays the groundwork for an automated tool for assessing cell health in a wide range of imaging experiments. Interpretability of such a method is crucial for its adoption. We take steps towards interpreting the classification mechanism of the trained ConvNet by visualizing salient features of images that contribute to accurate classification.