Microscopy

Viv is a JavaScript library providing utilities for rendering primary imaging data. Viv supports WebGL-based multi-channel rendering of both pyramidal and non-pyramidal images. The rendering components of Viv are provided as Deck.gl layers, facilitating image composition with existing layers and updating rendering properties within a reactive paradigm.

Deep learning based image restoration methods have recently been made available to restore images from under-exposed imaging conditions, increase spatio-temporal resolution (CARE) or self-supervised image denoising (Noise2Void). These powerful methods outperform conventional state-of-the-art methods and leverage down-stream analyses significantly such as segmentation and quantification.

To bring these new tools to a broader platform in the image analysis community, we developed a simple Jupyter based graphical user interface for CARE and Noise2Void, which lowers the burden for non-programmers and biologists to access these powerful methods in their daily routine.  CARE-less supports temporal, multi-channel image and volumetric data and many file formats by using the bioformats library. The user is guided through the different computation steps via inline documentation. For standard use cases, the graphical user interface exposes the most relevant parameters such as patch size and number of training iterations, while expert users still have access to advanced parameters such as U-net depth and kernel sizes. In addition, CARE-less provides visual outputs for training convergence and restoration quality. Any project settings can be stored and reused from command line for processing on compute clusters. The generated output files preserve important meta-data such as pixel sizes, axial spacing and time intervals.

VAST (Volume Annotation and Segmentation Tool) is a utility application for manual annotation of large EM stacks.

General labeling tool, used for a large variety of 3D data sets; electron-microscopic, multi-channel light-microscopic, and Micro-CT data sets as well as videos, and annotating arbitrary structures, regions and locations, depending on the user’s needs.

PSFj is a software tool that automatically analyses the full field-of-view (FOV) performance of a given fluorescence microscope/objective lens combination with respect to its optical resolution and chromatic aberrations. PSFj provides reporting functions to document the momentary performance of a system and it allows for the export of the obtained data, e.g. for image restoration purposes. PSFj is based on ImageJ and JAVA, and runs on Windows, Mac, and Linux PCs as a stand-alone application.

FastSME: Faster and Smoother Manifold Extraction From 3D Stack.

3D image stacks are routinely acquired to capture data that lie on undulating 3D manifolds yet processed in 2D by biologists. Algorithms to reconstruct the specimen morphology into a 2D representation from the 3D image volume are employed in such scenarios. In this paper, we present FastSME, which offers several improvements on the baseline SME algorithm which enables accurate 2D representation of data on a manifold from 3D volumes, however is computationally expensive. The improvements are achieved in terms of processing speed (3X-10X speed-up depending on image size), minimizing sensitivity to initialization, and also increases local smoothness of the recovered manifold resulting in better reconstructed 2D composite image. We compare the proposed FastSME against the baseline SME as well as other accessible state-of-the-art tools on synthetic and real microscopy data. Our evaluation on multiple metrics demonstrates the efficiency of the presented method in maintaining fidelity of manifold shape and hence specimen morphology.