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Deep learning in microscopy

The December 2019 issue of Nature Methods features a focus on Deep Learning in Microscopy. In this web collection, related content featured in the Nature journals is highlighted to celebrate these technological advances.

Recent research papers can be found under Research; Reviews, Perspectives, and news features under Comments and Reviews. These publications are selected by the editors of Nature Journals and this collection will be regularly updated throughout the year.


This analysis describes the results of three Cell Tracking Challenge editions for examining the performance of cell segmentation and tracking algorithms and provides practical feedback for users and developers.

Analysis | | Nature Methods

The 2018 Human Protein Atlas Image Classification competition sought to improve automated classification of protein subcellular localizations from fluorescence images. The winning strategies involved innovative deep learning approaches for multi-label classification.

Analysis | Open Access | | Nature Methods

Neural networks are a promising digital pathology tool but are often criticized for their limited explainability. Faust and others demonstrate how machine-learned features correlate with human-understandable histological patterns and groupings, permitting increased transparency of deep learning tools in medicine.

Article | | Nature Machine Intelligence

Volume electron microscopy data of brain tissue can tell us much about neural circuits, but increasingly large data sets demand automation of analysis. Here, the authors introduce cellular morphology neural networks and successfully automate a range of morphological analysis tasks.

Article | Open Access | | Nature Communications

Automated analysis of RNA localisation in smFISH data has been elusive. Here, the authors simulate and use a large dataset of images to design and validate a framework for highly accurate classification of sub-cellular RNA localisation patterns from smFISH experiments.

Article | Open Access | | Nature Communications

Cell protrusion dynamics are heterogeneous at the subcellular level, but current analyses operate at the cellular or ensemble level. Here the authors develop a computational framework to quantify subcellular protrusion phenotypes and reveal the underlying actin regulator dynamics at the leading edge.

Article | Open Access | | Nature Communications

LEAP is a deep-learning-based approach for the analysis of animal pose. LEAP’s graphical user interface facilitates training of the deep network. The authors illustrate the method by analyzing Drosophila and mouse behavior.

Article | | Nature Methods

webKnossos is a browser-based tracing and annotation tool for 3D electron microscopy data sets that is optimized for seamless data viewing. The tool’s flight-mode view facilitates fast neurite tracing because of its egocentric viewpoint.

Brief Communication | | Nature Methods

Comments and Reviews

Machine learning approaches that include deep learning are moving beyond image classification to change the way images are made.

Method to Watch | | Nature Methods

A deep network is best understood in terms of components used to design it—objective functions, architecture and learning rules—rather than unit-by-unit computation. Richards et al. argue that this inspires fruitful approaches to systems neuroscience.

Perspective | | Nature Neuroscience