Optimization of a Workflow for Biological Tissue Imaging and Analysis

Optimization of a Workflow for Biological Tissue Imaging and Analysis

Document Type

Abstract

Publication Date

10-2-2019

Abstract

Biomedical imaging for tissue analysis encompasses a range of techniques to represent samples. Biological images provide the basis for the study of histology at the micro and nanoscale level. Brightfield, widefield fluorescence, confocal, apotome, and electron microscopes are used to image tissue, cells and ultrastructural components and to generate single images or image stacks, in formats compatible with various analytical tools. Typically image stacks are digitally segmented using powerful image analysis tool, such as Amira, followed by rendering of 3D models from segmental 2D stacks. However, it is beneficial or crucial that users understand the tissue structure well enough to accurately perform segmentation. Misunderstanding the 2D images can lead to incorrect conclusions and false identification of biological elements. Therefore, we implemented a new workflow, involving the use of Amira for 2D stacks to segmentation, in conjunction with SyGlass for fast rendering of structures in virtual reality. Instead of a linear progression, the workflow using SyGlass allows for concurrent development of an Amira model along with a VR representation that improves the 3D render. Our experience thus far, using a recursive process have allowed for completion of segmentation and reconstruction with greater accuracy.

Project Mentor

Prof. P. Lafontant, PhD

Funding and Acknowledgements

Funding: NIH-NICHD, Buehler Biomedical Imaging Center, FITS-Tenzer

Optimization of a Workflow for Biological Tissue Imaging and Analysis

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