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Automatic detection and segmentation of biological objects in 2D and 3D image data is central for countless biomedical research questions to be answered. While many existing computational methods are used to reduce manual labeling time, there is still a huge demand for further quality improvements of automated solutions. In the natural image domain, spatial embedding-based instance segmentation methods are known to yield high-quality results, but their utility to biomedical data is largely unexplored. Here we introduce EmbedSeg, an embedding-based instance segmentation method designed to segment instances of desired objects visible in 2D or 3D biomedical image data. We apply our method to four 2D and seven 3D benchmark datasets, showing that we either match or outperform existing state-of-the-art methods. While the 2D datasets and three of the 3D datasets are well known, we have created the required training data for four new 3D datasets, which we make publicly available online. Next to performance, also usability is important for a method to be useful. Hence, EmbedSeg is fully open source (https://github.com/juglab/EmbedSeg), offering (i) tutorial notebooks to train EmbedSeg models and use them to segment object instances in new data, and (ii) a napari plugin that can also be used for training and segmentation without requiring any programming experience. We believe that this renders EmbedSeg accessible to virtually everyone who requires high-quality instance segmentations in 2D or 3D biomedical image data., Competing Interests: Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2022. Published by Elsevier B.V.)

In higher plants, germline differentiation occurs during a relatively short period within developing flowers. Understanding of the mechanisms that govern germline differentiation lags behind other plant developmental processes. This is largely because the germline is restricted to relatively few cells buried deep within floral tissues, which makes them difficult to study. To overcome this limitation, we have developed a methodology for live imaging of the germ cell lineage within floral organs of Arabidopsis using light sheet fluorescence microscopy. We have established reporter lines, cultivation conditions, and imaging protocols for high-resolution microscopy of developing flowers continuously for up to several days. We used multiview imagining to reconstruct a three-dimensional model of a flower at subcellular resolution. We demonstrate the power of this approach by capturing male and female meiosis, asymmetric pollen division, movement of meiotic chromosomes, and unusual restitution mitosis in tapetum cells. This method will enable new avenues of research into plant sexual reproduction., Competing Interests: SV, PM, JP, JK, MK, PB, SH, PT, KR No competing interests declared, (© 2020, Valuchova et al.)

State-of-the-art tissue-clearing methods provide subcellular-level optical access to intact tissues from individual organs and even to some entire mammals. When combined with light-sheet microscopy and automated approaches to image analysis, existing tissue-clearing methods can speed up and may reduce the cost of conventional histology by several orders of magnitude. In addition, tissue-clearing chemistry allows whole-organ antibody labelling, which can be applied even to thick human tissues. By combining the most powerful labelling, clearing, imaging and data-analysis tools, scientists are extracting structural and functional cellular and subcellular information on complex mammalian bodies and large human specimens at an accelerated pace. The rapid generation of terabyte-scale imaging data furthermore creates a high demand for efficient computational approaches that tackle challenges in large-scale data analysis and management. In this Review, we discuss how tissue-clearing methods could provide an unbiased, system-level view of mammalian bodies and human specimens and discuss future opportunities for the use of these methods in human neuroscience.

Unlabelled: Selective Plane Illumination Microscopy (SPIM) allows to image developing organisms in 3D at unprecedented temporal resolution over long periods of time. The resulting massive amounts of raw image data requires extensive processing interactively via dedicated graphical user interface (GUI) applications. The consecutive processing steps can be easily automated and the individual time points can be processed independently, which lends itself to trivial parallelization on a high performance computing (HPC) cluster. Here, we introduce an automated workflow for processing large multiview, multichannel, multiillumination time-lapse SPIM data on a single workstation or in parallel on a HPC cluster. The pipeline relies on snakemake to resolve dependencies among consecutive processing steps and can be easily adapted to any cluster environment for processing SPIM data in a fraction of the time required to collect it., Availability and Implementation: The code is distributed free and open source under the MIT license http://opensource.org/licenses/MIT The source code can be downloaded from github: https://github.com/mpicbg-scicomp/snakemake-workflows Documentation can be found here: http://fiji.sc/Automated_workflow_for_parallel_Multiview_Reconstruction, Contact: : schmied@mpi-cbg.de, Supplementary Information: Supplementary data are available at Bioinformatics online., (© The Author 2015. Published by Oxford University Press.)

OpenSPIM and OpenSpinMicroscopy emerged as open access platforms for Light Sheet and Optical Projection Imaging, often called as optical mesoscopy techniques. Both projects can be easily reproduced using comprehensive online instructions that should foster the implementation and further development of optical imaging techniques with sample rotation control. This additional dimension in an open system offers the possibility to make multi-view microscopy easily modified and will complement the emerging commercial solutions. Furthermore, it is deeply based on other open platforms such as MicroManager and Arduino, enabling development of tailored setups for very specific biological questions. In our perspective, the open access principle of OpenSPIM and OpenSpinMicroscopy is a game-changer, helping the concepts of light sheet and optical projection tomography (OPT) to enter the mainstream of biological imaging.