Automate Your Neuron Tracing and Neuroscientific Workflows

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.

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.