A neuron cell in the brain is traced and marked in a microscopy image in 3D view. The cell body is in the center and neurite branches sprout of it.
IMAGE ANALYSIS WORKFLOWS

Automate Your Neuron Tracing and Neuroscientific Workflows

Advanced Image Analysis Examples of Neuroscience Research Applications

Our innovative algorithms enable you to automatically extract neuron morphology from your image data. From neuron tracing to dendritic spine analysis, we’ve got you covered. You can choose between two published state-of-the-art algorithms to facilitate automated image analysis; threshold-based or probabilistic reconstruction. Our trace editing and proofreading tools allow for accurate results with a few clicks, taking your neuroscience research to a whole new scale.

A neuron cell is traced and marked in a microscopy image. Dendritic spines are differentiated and segmented in blue while the neuron cell body and neurite branches are segmented in orange.
A neuron cell is traced and marked in a microscopy image. Dendritic spines are differentiated and segmented in blue while the neuron cell body and neurite branches are segmented in orange.
Sample courtesy of R. Thomas and D. L. Benson, Icahn School of Medicine, Mount Sinai, New York, USA

Dendritic Spine Analysis

Examining Dendritic Spines and Neuronal Projections to Understand Neural Circuits

Microscopy and deep learning are valuable tools in Parkinson's research, allowing researchers to study neural circuits and understand the cellular mechanisms that regulate synapse formation and composition.

‍In this neuron tracing application, a deep learning-based semantic segmentation model was trained using ZEISS arivis Cloud to separate dendritic spines and neuronal projections using 3D z-stack images captured from a ZEISS Celldiscoverer 7 microscope. An iterative process involving data-centric model training was employed to refine the model before integrating it into an image analysis pipeline utilizing the 3D toolkit in ZEN.

The successful segmentation of dendritic spines using the trained model demonstrates the effectiveness of deep learning in complex image analysis and its potential to contribute to future neurological disease research.

Image analysis results in a 3D view of X-ray microscopy image. The segmentation highlights brain blood vessels (red), the nucleolus (green) within the nucleus (yellow), and the soma (blue).

Courtesy of Dr Kevin Boergens, University of Illinois at Chicago, USA.

3D Mouse Brain Segmentation

The 3D mouse brain section was imaged with the ZEISS XRM Versa and segmented in ZEISS arivis Pro using a custom-trained Deep Learning model. Diverse structures are clearly seen and marked in different colors. The segmentation highlights brain blood vessels in red, the nucleolus in green within the nucleus in yellow, and the soma in blue.

A screenshot of the 3D preview of image analysis results in ZEISS arivis Pro. A cube and set of yellow arrows dictate the area that is previewed. In it the neuron branches are traced from the cell body, and the track is marked.

Image acquired with ZEISS LSM 980 confocal microscope and analyzed with ZEISS arivis Pro. Original 3D tissue volume dataset (45GB CZI, confocal fluorescence microscopy, single channel) kindly provided by Dr. Steffen Burgold, ZEISS RMS Customer Center Oberkochen, Germany.

Automatic Neuron Tracing for Faster Results

Overcome the Challenge of Tracing Neurons in Microscopy Volume Imaging Data

The Automatic Neuron Tracing module in ZEISS arivis Pro allows analysis of even complex structural networks in both 2D or 3D multichannel image sets of virtually any size. The algorithm is based on the integration of two modern, published methods for neuron tracing, and was tested with 100s of GBs. State-of-the-art algorithms offers an easy-to-use approach with full flexibility and interactivity at all steps.

The novel software architecture delivers top performance in tracing applications for various imaging modalities. Combined with our best-in-class AI model training in ZEISS arivis Cloud, automatic tracing and trace editing of multidimensional data sets becomes an easy task, without the need to be an expert in bioinformatics. The new semi-automatic trace editing tool creates a neuron trace or branch interactively by drawing in the viewer with automatic detection of the path. You can use immersive VR to explore your samples and proofread results.

Two neuron cells traced and segmented, clearly marked in two different colors in a 3D view of a confocal fluorescence microscopy image.

Neuroscience Research without Compromises

Recorded Webinar

In this webinar Dr. Delisa Garcia and Dr. Kalliopi Arkoudi demonstrate how LSM Plus and Airyscan joint deconvolution (jDCV) enable enhanced resolution and signal to noise. Automated end-to-end image analysis Pipelines in ZEISS arivis Pro are based on state-of-the-art published and peer-reviewed algorithms. Allow multi-threaded processing of large datasets, suitable for neurons with/ without cell bodies for fast and efficient proofing and editing of tracing results.

Learn how to take your neuroscience to a whole new scale.

Automated 3D Neuron Tracing of Mouse Brain Neuronal Tissue

  • 3D tissue volume dataset kindly provided by Dr. Steffen Burgold, ZEISS RMS Customer Center Oberkochen, Germany.

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    * The images shown on this page represent research content. ZEISS explicitly excludes  the possibility of making a diagnosis or recommending treatment for possibly affected  patients  on the basis of  the information generated with an Axioscan 7 slide scanner.