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ASCB-EMBO 2019 Poster: Correlative Transformation and Visualization Tool for CLEM Analysis

Updated: Dec 6, 2019

Presentation time: Sunday Dec 8th at 12:00 PM

Poster number: P38/B39



The technology presented in this poster is a part of our Intelligent Connectomics Analysis platform that is being developed as part of the NIMH SBIR grant awarded to DRVision. You can read more about the grant here.


Abstract

Recent technological advancements in microscopy techniques, labeling methods, preparation procedure, data analysis informatics and computing infrastructures have made high‐resolution imaging and visualization of large tissue blocks a possibility. Armed by the promising advancements in elucidating and quantifying the connectomics of brain networks with subcellular resolution using electron microscopy (EM). There are increased interests in hybrid approaches correlating functional fluorescence microscopy data and ultrastructural information from EM in a common biological context, correlative light electron microscopy (CLEM). We are developing a correlative transformation and visualization tool to perform automated image registration for CLEM analysis. General purpose landmark and intensity‐based image registration approaches were implemented to register a LM image with a corresponding EM image. Both rigid registration and non‐rigid registration methods were developed and applied to the CLEM dataset for comparisons. The 3D image viewer supports correlative visualization of 3D LM and EM volumes in a single window. The functionality of clipping/orthographic planes enables viewing of the volume cross sections in the X, Y, and Z directions, or in any arbitrary orientations. This feature becomes very useful when certain objects could only be observed or found in a certain orientation. We validated the correlative transformation and visualization tool using a retina CLEM dataset acquired at the Wong Lab. The tool is integrated with the 3D visualization environment in Aivia for fast viewing of registered LM and EM volumes. This greatly speeds up the studies for neuron circuitry identification and novel cell type discoveries. We will continue to improve the correlative transformation and visualization tool in an Aivia prototype by introducing more 3D visualization options and adding support for measurements and data structures for correlative studies.

Here we describe a novel AI assisted tools that will automate the labor-intensive task of manual neuron reconstruction, synapse detection, and circuit inference based on EM and CLEM images. These tools have the potential to accelerate the rate of knowledge creation in the field of neurosciences.


Authors

C. Huang, W. Yu, H. Sasaki, R. Wong, S. J. Lee, L. A. G. Lucas

  • W. Yu and R. Wong are a part of the Department of Biological Structure at the University of Washington, Seattle, WA.

  • Other authors are part of DRVision Technologies LLC, Bellevue, WA