Democratising deep learning for microscopy with ZeroCostDL4Mic

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von Chamier, Lucas | Laine, Romain, F | Jukkala, Johanna | Spahn, Christoph | Krentzel, Daniel | Nehme, Elias | Lerche, Martina | Hernández-Pérez, Sara | Mattila, Pieta, K | Karinou, Eleni | Holden, Séamus | Solak, Ahmet, Can | Krull, Alexander | Buchholz, Tim-Oliver | Jones, Martin, L | Royer, Loïc, A | Leterrier, Christophe | Shechtman, Yoav | Jug, Florian | Heilemann, Mike | Jacquemet, Guillaume | Henriques, Ricardo

Edité par CCSD ; Nature Publishing Group -

International audience. Deep Learning (DL) methods are powerful analytical tools for microscopy and can outperform conventional image processing pipelines. Despite the enthusiasm and innovations fuelled by DL technology, the need to access powerful and compatible resources to train DL networks leads to an accessibility barrier that novice users often find difficult to overcome. Here, we present ZeroCostDL4Mic, an entry-level platform simplifying DL access by leveraging the free, cloud-based computational resources of Google Colab. ZeroCostDL4Mic allows researchers with no coding expertise to train and apply key DL networks to perform tasks including segmentation (using U-Net and StarDist), object detection (using YOLOv2), denoising (using CARE and Noise2Void), super-resolution microscopy (using Deep-STORM), and image-to-image translation (using Label-free prediction-fnet, pix2pix and CycleGAN). Importantly, we provide suitable quantitative tools for each network to evaluate model performance, allowing model optimisation. We demonstrate the application of the platform to study multiple biological processes.

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