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

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Title: Transformation diffusion reconstruction of three-dimensional histology volumes from two-dimensional image stacks
Authors: Casero, R
Siedlecka, U
Jones, ES
Gruscheski, L
Gibb, M
Schneider, JE
Kohl, P
Grau, V
Item Type: Journal Article
Abstract: 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.
Issue Date: 23-Mar-2017
Date of Acceptance: 21-Mar-2017
URI: http://hdl.handle.net/10044/1/48794
DOI: http://dx.doi.org/10.1016/j.media.2017.03.004
ISSN: 1361-8423
Publisher: Elsevier
Start Page: 184
End Page: 204
Journal / Book Title: Medical Image Analysis
Volume: 38
Copyright Statement: © 2017 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY license. ( http://creativecommons.org/licenses/by/4.0/ )
Sponsor/Funder: British Heart Foundation
Commission of the European Communities
British Heart Foundation
Funder's Grant Number: PG/09/066/27898
Keywords: Science & Technology
Life Sciences & Biomedicine
Computer Science, Artificial Intelligence
Computer Science, Interdisciplinary Applications
Engineering, Biomedical
Radiology, Nuclear Medicine & Medical Imaging
Computer Science
Diffusion equation
B-spline transformation
Histology reconstruction
Nuclear Medicine & Medical Imaging
09 Engineering
11 Medical And Health Sciences
Publication Status: Published
Appears in Collections:National Heart and Lung Institute