We are extremely happy to announce that our paper “KymoButler, a deep learning software for automated kymograph analysis” has been published on the eLife journal. You can find our press release here, and an eLife digest explaining our work for a non-specialised audience here.

But we haven’t just been constantly checking our mailboxes waiting for good news from eLife – in the past few months we have been busy making all of our software run seamlessly on the cloud, so that you can access it from anywhere and at anytime without any installation! If you work with kymographs and still haven’t tried KymoButler, give it a go here. We also updated KymoButler with some minor fixes and improved stability, have a look at KymoButler’s changelog for more details!

And more is coming! We are now further optimising the KymoButler code and planning our first neural network retraining with new data to further improve performance. We are also brewing more software that will make your image data analysis easier if you work with cell data and/or nuclei data… stay tuned for more info!

Finally, if your work requires manual annotations or quantitative data analysis, and you want to speed up and remove biases from your workflow, get in touch with us and we will find the best solution for your specific application!

Title image credit: Eva Pillai


Andrea Dimitracopoulos

Co-Founder of deepMirror.ai Postdoc in Neuroscience at the University of Cambridge MRes and PhD in Modelling Biological Complexity MSc and BSc in Biomedical Engineering

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