Imperial College London
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ProfessorPeterKohl

Faculty of Medicine, National Heart & Lung Institute

Visiting Professor
 
 
 
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Contact

 

p.kohl Website

 
 
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Location

 

Heart Science CentreHarefield Hospital

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Summary

 

Publications

Citation

BibTex format

@article{Casero:2017:10.1016/j.media.2017.03.004,
author = {Casero, R and Siedlecka, U and Jones, ES and Gruscheski, L and Gibb, M and Schneider, JE and Kohl, P and Grau, V},
doi = {10.1016/j.media.2017.03.004},
journal = {Medical Image Analysis},
pages = {184--204},
title = {Transformation diffusion reconstruction of three-dimensional histology volumes from two-dimensional image stacks},
url = {http://dx.doi.org/10.1016/j.media.2017.03.004},
volume = {38},
year = {2017}
}
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RIS format (EndNote, RefMan)

TY  - JOUR
AB  - 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
AU  - Casero,R
AU  - Siedlecka,U
AU  - Jones,ES
AU  - Gruscheski,L
AU  - Gibb,M
AU  - Schneider,JE
AU  - Kohl,P
AU  - Grau,V
DO  - 10.1016/j.media.2017.03.004
EP  - 204
PY  - 2017///
SN  - 1361-8423
SP  - 184
TI  - Transformation diffusion reconstruction of three-dimensional histology volumes from two-dimensional image stacks
T2  - Medical Image Analysis
UR  - http://dx.doi.org/10.1016/j.media.2017.03.004
UR  - http://hdl.handle.net/10044/1/48794
VL  - 38
ER  -
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