Performing Video Frame Prediction of Microbial Growth with a Recurrent Neural Network
This work addresses video frame prediction for microbial growth, potentially aiding autonomous microbiology experiments, but it appears incremental as it applies an existing RNN method to a new dataset without major methodological innovations.
The researchers tackled the problem of predicting microbial growth video frames using a Recurrent Neural Network (RNN) trained on fluorescence microscopy data, achieving accurate predictions for the last 10 frames of 20-frame videos as assessed by image, population curve, and colony metrics.
A Recurrent Neural Network (RNN) was used to perform video frame prediction of microbial growth for a population of two mutants of Pseudomonas aeruginosa. The RNN was trained on videos of 20 frames that were acquired using fluorescence microscopy and microfluidics. The network predicted the last 10 frames of each video, and the accuracy's of the predictions was assessed by comparing raw images, population curves, and the number and size of individual colonies. Overall, we found the predictions to be accurate using this approach. The implications this result has on designing autonomous experiments in microbiology, and the steps that can be taken to make the predictions even more accurate, are discussed.