Simple Tools for 3-Dimensional Image Processing

1 NSH Contact Hour(s)

(based on 2 customer ratings)

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Presented by Sean McKinney, Ph.D., Stowers Institute for Medical Research

Large, 3-dimensional datasets are commonplace now with platforms like selective plane illumination (SPIM), spinning disk microscopy, and serial section imaging. Reconstructing 3-dimensional visualizations from these datasets can be cpu and memory intensive, but can be done with relatively simple tools that are freely available. Here we will look at reconstructing a sectioned dataset using FiJi. We will also take a look at finding and quantifying cells automatically on a 3-dimensional confocal dataset.

See more courses in: Digital Pathology

Continuing Education Credits

NSH Contact Hours (acceptable for AMT, ASCP, and state recertification): 1 hour(s)

Course number VIR121217

Objectives

Provide an introduction on Datasets and Fiji
Discuss Serial section 3D reconstruction from slide scanner data
3D image quantification from whole animals or thick slices

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(based on 2 customer ratings)

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Additional Information

Course Description: Large, 3-dimensional datasets are commonplace now with platforms like selective plane illumination (SPIM), spinning disk microscopy, and serial section imaging. Reconstructing 3-dimensional visualizations from these datasets can be cpu and memory intensive, but can be done with relatively simple tools that are freely available. Here we will look at reconstructing a sectioned dataset using FiJi. We will also take a look at finding and quantifying cells automatically on a 3-dimensional confocal dataset.

Presenter Bio: Dr. McKinney received his PHD in physics from the University of Illinois and Sean is a microscopy Group Head at the Stowers Institute for Medical Research, where he manages all daily operations as well as working on research projects. He has built the Institute’s homemade SPIM and PALM rigs and has developed fluorescent proteins.

Additional Information: This was presented as part of the 2017 VIR webinar series.

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