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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., Deep learning methods show great promise for the analysis of microscopy images but there is currently an accessibility barrier to many users. Here the authors report a convenient entry-level deep learning platform that can be used at no cost: ZeroCostDL4Mic.

Recent developments in Oblique Plane Microscopy (OPM) have shown that it can achieve high spatio-temporal resolution. Here we describe a single objective light-sheet microscope based on oblique plane illumination that achieves: (i) large field of view and high-resolution imaging via a custom remote focusing objective; (ii) fast volumetric imaging by means of light-sheet stabilised stage scanning – a novel scanning modality that extends the imaging volume without compromising imaging speed nor quality; (iii) multi-view imaging by alternating the orientation of light-sheet illumination and detection to improve the image quality on large samples; (iv) simpler design and ergonomics by remote placement of coverslips to allow inverted imaging, enabling imaging across scales in a high-throughput format. Overall, we achieved a resolution of 450 nm laterally and 2 μm axially and a field of view of 3000 μm × 800 μm × 300 μm. We demonstrate the speed, field of view, resolution and versatility of our novel instrument by imaging various systems, including zebrafish whole brain activity, Drosophila egg chamber development, and zebrafish development – up to nine embryos simultaneously.

The resources and expertise needed to use Deep Learning (DL) in bioimaging remain significant barriers for most laboratories. We present https://github.com/HenriquesLab/ZeroCostDL4Mic/wiki, a platform simplifying access to DL by exploiting the free, cloud-based computational resources of Google Colab. https://github.com/HenriquesLab/ZeroCostDL4Mic/wiki allows researchers to train, evaluate, and apply key DL networks to perform tasks including segmentation, detection, denoising, restoration, resolution enhancement and image-to-image translation. We demonstrate the application of the platform to study multiple biological processes.