Biology subjects

Günther, U.

Publications and source records attributed to Günther, U..

3 recordsLinked to original sources

bioRxiv2020

SNT: A Unifying Toolbox for Quantification of Neuronal Anatomy

Quantification of neuronal morphology is essential for understanding neuronal connectivity and many software tools have been developed for neuronal reconstruction and morphometry. However, such tools remain domain-specific, tethered to specific imaging modalities, and were not designed to accommodate the rich metadata generated by recent whole-brain cellular connectomics. To address these limitations, we created SNT: a unifying framework for neuronal morphometry and analysis of single-cell connectomics for the widely used Fiji and ImageJ platforms. We demonstrate that SNT can be used to tackle important problems in contemporary neuroscience, validate its utility, and illustrate how it establishes an end-to-end platform for tracing, proof-editing, visualization, quantification, and modeling of neuroanatomy. With an open and scriptable architecture, a large user base, and thorough community-based documentation, SNT is an accessible and scalable resource for the broad neuroscience community that synergizes well with existing software.

neuroscience↗

bioRxiv2018

Multi-sample SPIM image acquisition, processing and analysis of vascular growth in zebrafish

To quantitatively understand biological processes that occur over many hours or days, it is desirable to image multiple samples simultaneously and automatically process and analyze the resulting datasets. Here, we present a complete multi-sample preparation, imaging, processing, and analysis workflow to determine the development of the vascular volume in zebrafish. Up to five live embryos were mounted and imaged simultaneously over several days using selective plane illumination microscopy (SPIM). The resulting large imagery dataset of several terabytes was processed in an automated manner on a high-performance computer cluster and segmented with a novel segmentation approach that uses images of red blood cells as training data. This analysis yielded a precise quantification of growth characteristics of the whole vascular network, head vasculature, and tail vasculature over development. Our multi-sample platform demonstrates effective upgrades to conventional single-sample imaging platforms and paves the way for diverse quantitative long-term imaging studies.\n\nSummary statementWe present a dedicated multi-sample light sheet imaging, processing and analysis platform and demonstrate its value for studies of vascular growth in zebrafish.

developmental biology↗

bioRxiv2018

Forget Pixels: Adaptive Particle Representation of Fluorescence Microscopy Images

Modern microscopy modalities create a data deluge with gigabytes of data generated each second, and terabytes per day. Storing and processing these data is a severe bottleneck, not fully alleviated by data compression. We argue that this is because images are processed as regular grids of pixels. To address the root of the problem, we here propose a content-adaptive representation of fluorescence microscopy images called the Adaptive Particle Representation (APR). The APR replaces the regular grid of pixels with particles positioned according to image content. This overcomes storage bottlenecks, as data compression does, but additionally overcomes memory and processing bottlenecks, since the APR can directly be used in processing without going back to pixels. We present the ideas, concepts, and algorithms of the APR and validate them using noisy 3D image data. We show that the APR represents the content of an image while maintaining image quality. We then show that the adaptivity of the APR provides orders of magnitude benefits across a range of image processing tasks. Therefore, the APR provides a simple, extendable, and efficient content-aware representation of images that relaxes current data and processing bottlenecks.

bioinformatics↗