ImageJ is an image analysis program extensively used in the biological sciences and beyond. Due to its ease of use, recordable macro language, and extensible plug-in architecture, ImageJ enjoys contributions from non-programmers, amateur programmers, and professional developers alike. Enabling such a diversity of contributors has resulted in a large community that spans the biological and physical sciences. However, a rapidly growing user base, diverging plugin suites, and technical limitations have revealed a clear need for a concerted software engineering effort to support emerging imaging paradigms, to ensure the software’s ability to handle the requirements of modern science. We rewrote the entire ImageJ codebase, engineering a redesigned plugin mechanism intended to facilitate extensibility at every level, with the goal of creating a more powerful tool that continues to serve the existing community while addressing a wider range of scientific requirements. This next-generation ImageJ, called “ImageJ2” in places where the distinction matters, provides a host of new functionality. It separates concerns, fully decoupling the data model from the user interface. It emphasizes integration with external applications to maximize interoperability. Its robust new plugin framework allows everything from image formats, to scripting languages, to visualization to be extended by the community. The redesigned data model supports arbitrarily large, N-dimensional datasets, which are increasingly common in modern image acquisition. Despite the scope of these changes, backwards compatibility is maintained such that this new functionality can be seamlessly integrated with the classic ImageJ interface, allowing users and developers to migrate to these new methods at their own pace. Scientific imaging benefits from open-source programs that advance new method development and deployment to a diverse audience. ImageJ has continuously evolved with this idea in mind; however, new and emerging scientific requirements have posed corresponding challenges for ImageJ’s development. The described improvements provide a framework engineered for flexibility, intended to support these requirements as well as accommodate future needs. Future efforts will focus on implementing new algorithms in this framework and expanding collaborations with other popular scientific software suites.

/pdf/imagej2-imagej-for-the-next-generation-of-scientific-image-41ybjcosb6.pdf

ImageJ2: ImageJ for the next generation of scientific image data

ImageJ is an image analysis program extensively used in the biological sciences and beyond. Due to its ease of use, recordable macro language, and extensible plug-in architecture, ImageJ enjoys contributions from non-programmers, amateur programmers, and professional developers alike. Enabling such a diversity of contributors has resulted in a large community that spans the biological and physical sciences. However, a rapidly growing user base, diverging plugin suites, and technical limitations have revealed a clear need for a concerted software engineering effort to support emerging imaging paradigms, to ensure the software's ability to handle the requirements of modern science. Due to these new and emerging challenges in scientific imaging, ImageJ is at a critical development crossroads. 
We present ImageJ2, a total redesign of ImageJ offering a host of new functionality. It separates concerns, fully decoupling the data model from the user interface. It emphasizes integration with external applications to maximize interoperability. Its robust new plugin framework allows everything from image formats, to scripting languages, to visualization to be extended by the community. The redesigned data model supports arbitrarily large, N-dimensional datasets, which are increasingly common in modern image acquisition. Despite the scope of these changes, backwards compatibility is maintained such that this new functionality can be seamlessly integrated with the classic ImageJ interface, allowing users and developers to migrate to these new methods at their own pace. ImageJ2 provides a framework engineered for flexibility, intended to support these requirements as well as accommodate future needs.

Recent advances in computer vision and machine learning underpin a collection of algorithms with an impressive ability to decipher the content of images. These deep learning algorithms are being applied to biological images and are transforming the analysis and interpretation of imaging data. These advances are positioned to render difficult analyses routine and to enable researchers to carry out new, previously impossible experiments. Here we review the intersection between deep learning and cellular image analysis and provide an overview of both the mathematical mechanics and the programming frameworks of deep learning that are pertinent to life scientists. We survey the field's progress in four key applications: image classification, image segmentation, object tracking, and augmented microscopy. Last, we relay our labs' experience with three key aspects of implementing deep learning in the laboratory: annotating training data, selecting and training a range of neural network architectures, and deploying solutions. We also highlight existing datasets and implementations for each surveyed application.

Deep learning for cellular image analysis

The mechanism by which bacteria divide is not well understood. Cell division is mediated by filaments of FtsZ and FtsA (FtsAZ) that recruit septal peptidoglycan-synthesizing enzymes to the division site. To understand how these components coordinate to divide cells, we visualized their movements relative to the dynamics of cell wall synthesis during cytokinesis. We found that the division septum was built at discrete sites that moved around the division plane. FtsAZ filaments treadmilled circumferentially around the division ring and drove the motions of the peptidoglycan-synthesizing enzymes. The FtsZ treadmilling rate controlled both the rate of peptidoglycan synthesis and cell division. Thus, FtsZ treadmilling guides the progressive insertion of new cell wall by building increasingly smaller concentric rings of peptidoglycan to divide the cell.

/pdf/treadmilling-by-ftsz-filaments-drives-peptidoglycan-ged310716h.pdf

Treadmilling by FtsZ filaments drives peptidoglycan synthesis and bacterial cell division

Phototoxicity frequently occurs during live fluorescence microscopy, and its consequences are often underestimated. Damage to cellular macromolecules upon excitation light illumination can impair sample physiology, and even lead to sample death. In this review, we explain how phototoxicity influences live samples, and we highlight that, besides the obvious effects of phototoxicity, there are often subtler consequences of illumination that are imperceptible when only the morphology of samples is examined. Such less apparent manifestations of phototoxicity are equally problematic, and can change the conclusions drawn from an experiment. Thus, limiting phototoxicity is a prerequisite for obtaining reproducible quantitative data on biological processes. We present strategies to reduce phototoxicity, e.g. limiting the illumination to the focal plane and suggest controls for phototoxicity effects. Overall, we argue that phototoxicity needs increased attention from researchers when designing experiments, and when evaluating research findings.

/pdf/phototoxicity-in-live-fluorescence-microscopy-and-how-to-brqc20l2rq.pdf

Phototoxicity in live fluorescence microscopy, and how to avoid it.

https://hal-pasteur.archives-ouvertes.fr/pasteur-01799353/document

Nick Perry

Papers

TrackMate: An open and extensible platform for single-particle tracking.