COMPUTATIONAL RESEARCH AND APPLICATIONS SEMINAR – Transitioning Microscopy 2D to 3D for Tumor Biology and Pathology – Lockett

When:
October 16, 2017 @ 4:30 pm – 5:45 pm
2017-10-16T16:30:00-04:00
2017-10-16T17:45:00-04:00
Where:
Exploratory Hall, Room 3301, Fairfax Campus
Cost:
Free
Contact:
Joseph Marr
703-993-5017

COMPUTATIONAL RESEARCH AND APPLICATIONS SEMINAR

Stephen Lockett, PhD
Principal Scientist and Director of the Optical Microscopy and Analysis Laboratory
Frederick National Laboratory for Cancer Research, National Cancer Institute

Transitioning Microscopy from 2D to 3D for Tumor Biology and Pathology

Monday, October 16, 4:30-5:45
Exploratory Hall, Room 3301

 

Abstract: Discerning the 3D context of individual cells in tissue is fundamental for understanding tissue development, homeostasis in healthy adult organs and tumor emergence.  Determining this context, which requires 3D microscopic imaging to 100s of microns depth into tissues, is hampered in multiple ways: (1) penetration of fluorescent dyes deep into tissue, (2) absorption and scattering of light, (3) sufficiently high speed image acquisition and (4) automated image analysis, because datasets are too large for human visual interpretation.  Furthermore, in the case of tumors, standard practice in clinical pathology is to only image a thin slice about 5 microns thick, which has several limitations: there is significant inter-observer variability in the diagnosis, the slice can miss the tumor and determining the spatial relationships of cells to each other is incomplete.  To address these limitations, we have evaluated tissue clearing protocols, which reduce scattering and spherical aberration, and we have achieved high spatial resolution imaging up 0.35 mm depth utilizing spinning disk confocal microscopy or lightsheet microscopy for high speed image acquisition.  The image analysis tasks for 3D images are much more demanding than 2D images for the following reasons: (1) visualization of the entirety of each object (cell or cell nucleus) is not possible in a single display, (2) purely interactive segmentation is impractical even for one object, (3) automatic algorithms are imperfect, and (4) in the case of basic research relatively few cells are imaged increasing the need to analyze each cell as accurately as possible.  Consequently, we are working on tools that merge automatic segmentation algorithms, computer vision and human annotation for delineating objects in 3D images.  We have developed a graphcut-based algorithm for finding the globally-optimal surface delineating each manually seeded nucleus.  The algorithm is restricted to point convex objects, which is generally satisfactory for nuclei, but not for entire cells that can have arbitrary shapes.  For whole cell segmentation in 3D, our approach is slice-by-slice based.  In the first 2D image slice (approximately through the center of the cell), the user draws an approximate 2D border and the border is automatically optimized using active contour modeling.  This optimized border then serves as the approximate border for adjacent slices, which are in turn automatically optimized.  The method is accurate except where the cell surface is in the plane of the slice, so a next step is to facilitate segmenting each cell in different orientations.    We are starting to utilize this technology to understand the complex interplay between cancer and normal cells, particularly immune system cells and supporting mesenchyme leading to tumor growth or regression in the case of treatment.  The research was funded by NCI Contract No. HHSN261200800001E and supported in part by the Intramural Research Program of the National Institutes of Health, National Cancer Institute, Center for Cancer Research.

Stephen Lockett received the Ph.D. degree from the Department of Medicine, Birmingham University, England.  He has published over 120 research papers and has received several international awards.  His research interests include fluorescence microscopy and the development of analysis software for extracting quantitative information from images.