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Transformation diffusion reconstruction of three-dimensional histology volumes from two-dimensional image stacks

Traditional histology is the gold standard for tissue studies, but it is intrinsically reliant on two-dimensional (2D) images. Study of volumetric tissue samples such as whole hearts produces a stack of misaligned and distorted 2D images that need to be reconstructed to recover a congruent volume wi...

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Autores principales: Casero, Ramón, Siedlecka, Urszula, Jones, Elizabeth S., Gruscheski, Lena, Gibb, Matthew, Schneider, Jürgen E., Kohl, Peter, Grau, Vicente
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Elsevier 2017
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5408912/
https://www.ncbi.nlm.nih.gov/pubmed/28411458
http://dx.doi.org/10.1016/j.media.2017.03.004
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author Casero, Ramón
Siedlecka, Urszula
Jones, Elizabeth S.
Gruscheski, Lena
Gibb, Matthew
Schneider, Jürgen E.
Kohl, Peter
Grau, Vicente
author_facet Casero, Ramón
Siedlecka, Urszula
Jones, Elizabeth S.
Gruscheski, Lena
Gibb, Matthew
Schneider, Jürgen E.
Kohl, Peter
Grau, Vicente
author_sort Casero, Ramón
collection PubMed
description Traditional histology is the gold standard for tissue studies, but it is intrinsically reliant on two-dimensional (2D) images. Study of volumetric tissue samples such as whole hearts produces a stack of misaligned and distorted 2D images that need to be reconstructed to recover a congruent volume with the original sample's shape. In this paper, we develop a mathematical framework called Transformation Diffusion (TD) for stack alignment refinement as a solution to the heat diffusion equation. This general framework does not require contour segmentation, is independent of the registration method used, and is trivially parallelizable. After the first stack sweep, we also replace registration operations by operations in the space of transformations, several orders of magnitude faster and less memory-consuming. Implementing TD with operations in the space of transformations produces our Transformation Diffusion Reconstruction (TDR) algorithm, applicable to general transformations that are closed under inversion and composition. In particular, we provide formulas for translation and affine transformations. We also propose an Approximated TDR (ATDR) algorithm that extends the same principles to tensor-product B-spline transformations. Using TDR and ATDR, we reconstruct a full mouse heart at pixel size 0.92 µm × 0.92 µm, cut 10 µm thick, spaced 20 µm (84G). Our algorithms employ only local information from transformations between neighboring slices, but the TD framework allows theoretical analysis of the refinement as applying a global Gaussian low-pass filter to the unknown stack misalignments. We also show that reconstruction without an external reference produces large shape artifacts in a cardiac specimen while still optimizing slice-to-slice alignment. To overcome this problem, we use a pre-cutting blockface imaging process previously developed by our group that takes advantage of Brewster's angle and a polarizer to capture the outline of only the topmost layer of wax in the block containing embedded tissue for histological sectioning.
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spelling pubmed-54089122017-05-05 Transformation diffusion reconstruction of three-dimensional histology volumes from two-dimensional image stacks Casero, Ramón Siedlecka, Urszula Jones, Elizabeth S. Gruscheski, Lena Gibb, Matthew Schneider, Jürgen E. Kohl, Peter Grau, Vicente Med Image Anal Article Traditional histology is the gold standard for tissue studies, but it is intrinsically reliant on two-dimensional (2D) images. Study of volumetric tissue samples such as whole hearts produces a stack of misaligned and distorted 2D images that need to be reconstructed to recover a congruent volume with the original sample's shape. In this paper, we develop a mathematical framework called Transformation Diffusion (TD) for stack alignment refinement as a solution to the heat diffusion equation. This general framework does not require contour segmentation, is independent of the registration method used, and is trivially parallelizable. After the first stack sweep, we also replace registration operations by operations in the space of transformations, several orders of magnitude faster and less memory-consuming. Implementing TD with operations in the space of transformations produces our Transformation Diffusion Reconstruction (TDR) algorithm, applicable to general transformations that are closed under inversion and composition. In particular, we provide formulas for translation and affine transformations. We also propose an Approximated TDR (ATDR) algorithm that extends the same principles to tensor-product B-spline transformations. Using TDR and ATDR, we reconstruct a full mouse heart at pixel size 0.92 µm × 0.92 µm, cut 10 µm thick, spaced 20 µm (84G). Our algorithms employ only local information from transformations between neighboring slices, but the TD framework allows theoretical analysis of the refinement as applying a global Gaussian low-pass filter to the unknown stack misalignments. We also show that reconstruction without an external reference produces large shape artifacts in a cardiac specimen while still optimizing slice-to-slice alignment. To overcome this problem, we use a pre-cutting blockface imaging process previously developed by our group that takes advantage of Brewster's angle and a polarizer to capture the outline of only the topmost layer of wax in the block containing embedded tissue for histological sectioning. Elsevier 2017-05 /pmc/articles/PMC5408912/ /pubmed/28411458 http://dx.doi.org/10.1016/j.media.2017.03.004 Text en © 2017 The Authors http://creativecommons.org/licenses/by/4.0/ This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Casero, Ramón
Siedlecka, Urszula
Jones, Elizabeth S.
Gruscheski, Lena
Gibb, Matthew
Schneider, Jürgen E.
Kohl, Peter
Grau, Vicente
Transformation diffusion reconstruction of three-dimensional histology volumes from two-dimensional image stacks
title Transformation diffusion reconstruction of three-dimensional histology volumes from two-dimensional image stacks
title_full Transformation diffusion reconstruction of three-dimensional histology volumes from two-dimensional image stacks
title_fullStr Transformation diffusion reconstruction of three-dimensional histology volumes from two-dimensional image stacks
title_full_unstemmed Transformation diffusion reconstruction of three-dimensional histology volumes from two-dimensional image stacks
title_short Transformation diffusion reconstruction of three-dimensional histology volumes from two-dimensional image stacks
title_sort transformation diffusion reconstruction of three-dimensional histology volumes from two-dimensional image stacks
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5408912/
https://www.ncbi.nlm.nih.gov/pubmed/28411458
http://dx.doi.org/10.1016/j.media.2017.03.004
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