The point spread function (PSF) is fundamental to any type of microscopy, most importantly so for single-molecule localization techniques, where the exact PSF shape is crucial for precise molecule localization at the nanoscale. Optical aberrations and fixed fluorophore dipoles often result in non-isotropic and distorted PSFs, impairing and biasing conventional fitting approaches. Further, PSF shapes are deliberately modified in PSF engineering approaches for providing improved sensitivity, e.g., for 3D localization or determination of dipole orientation. As this can lead to highly complex PSF shapes, a tool for visualizing expected PSFs would facilitate the interpretation of obtained data and the design of experimental approaches. To this end, we introduce a comprehensive and accessible computer application that allows for the simulation of realistic PSFs based on the full vectorial PSF model. Our tool incorporates a wide range of microscope and fluorophore parameters, including orientationally constrained fluorophores, as well as custom aberrations, transmission and phase masks, thus enabling an accurate representation of various imaging conditions. An additional feature is the simulation of crowded molecular environments with overlapping PSFs. Further, our app directly provides the Cramér–Rao bound for assessing the best achievable localization precision under given conditions. Finally, our software allows for the fitting of custom aberrations directly from experimental data, as well as the generation of a large dataset with randomized simulation parameters, effectively bridging the gap between simulated and experimental scenarios, and enhancing experimental design and result validation.

The reconstruction of neural circuits from serial section electron microscopy (ssEM) images is being accelerated by automatic image segmentation methods. Segmentation accuracy is often limited by the preceding step of aligning 2D section images to create a 3D image stack. Precise and robust alignment in the presence of image artifacts is challenging, especially as datasets are attaining the petascale. We present a computational pipeline for aligning ssEM images with several key elements. Self-supervised convolutional nets are trained via metric learning to encode and align image pairs, and they are used to initialize iterative fine-tuning of alignment. A procedure called vector voting increases robustness to image artifacts or missing image data. For speedup the series is divided into blocks that are distributed to computational workers for alignment. The blocks are aligned to each other by composing transformations with decay, which achieves a global alignment without resorting to a time-consuming global optimization. We apply our pipeline to a whole fly brain dataset, and show improved accuracy relative to prior state of the art. We also demonstrate that our pipeline scales to a cubic millimeter of mouse visual cortex. Our pipeline is publicly available through two open source Python packages.

Many scientific software platforms provide plugin mechanisms that simplify the integration, deployment, and execution of externally developed functionality. One of the most widely used platforms in the imaging space is Fiji, a popular open-source application for scientific image analysis. Fiji incorporates and builds on the ImageJ and ImageJ2 platforms, which provide a powerful plugin architecture used by thousands of plugins to solve a wide variety of problems. This capability is a major part of Fiji's success, and it has become a widely used biological image analysis tool and a target for new functionality. However, a plugin-based software architecture cannot unify disparate platforms operating on incompatible data structures; interoperability necessitates the creation of adaptation or "bridge" layers to translate data and invoke functionality. As a result, while platforms like Fiji enable a high degree of interconnectivity and extensibility, they were not fundamentally designed to integrate across the many data types, programming languages, and architectural differences of various software help address this challenge, we present SciJava Ops, a foundational software library for expressing algorithms as plugins in a unified and extensible way. Continuing the evolution of Fiji's SciJava plugin mechanism, SciJava Ops enables users to harness algorithms from various software platforms within a central execution environment. In addition, SciJava Ops automatically adapts data into the most appropriate structure for each algorithm, allowing users to freely and transparently combine algorithms from otherwise incompatible tools. While SciJava Ops is initially distributed as a Fiji update site, the framework does not require Fiji, ImageJ, or ImageJ2, and would be suitable for integration with additional image analysis platforms.