Immunofluorescence-guided segmentation of three-dimensional features in micro-computed tomography datasets of human lung tissue
Immunofluorescence-guided segmentation of three-dimensional features in micro-computed tomography datasets of human lung tissue
Micro-computed tomography (µCT) provides non-destructive three-dimensional (3D) imaging of soft tissue microstructures. Specific features in µCT images can be identified using correlated two-dimensional (2D) histology images allowing manual segmentation. However, this is very time-consuming and requires specialist knowledge of the tissue and imaging modalities involved. Using a custom-designed µCT system optimized for imaging unstained formalin-fixed paraffin-embedded soft tissues, we imaged human lung tissue at isotropic voxel sizes less than 10 µm. Tissue sections were stained with haematoxylin and eosin or cytokeratin 18 in columnar airway epithelial cells using immunofluorescence (IF), as an exemplar of this workflow. Novel utilization of tissue autofluorescence allowed automatic alignment of 2D microscopy images to the 3D µCT data using scripted co-registration and automated image warping algorithms. Warped IF images, which were accurately aligned with the µCT datasets, allowed 3D segmentation of immunoreactive tissue microstructures in the human lung. Blood vessels were segmented semi-automatically using the co-registered µCT datasets. Correlating 2D IF and 3D µCT data enables accurate identification, localization and segmentation of features in fixed soft lung tissue. Our novel correlative imaging workflow provides faster and more automated 3D segmentation of µCT datasets. This is applicable to the huge range of formalin-fixed paraffin-embedded tissues held in biobanks and archives.
SIFT, blood vessel networks, correlative imaging, histology, registration, warping
Lawson, Matthew
18483732-1f57-4c2c-bccb-0df94f4c71ac
Katsamenis, Orestis L.
8553e7c3-d860-4b7a-a883-abf6c0c4b438
Chatelet, David
6371fd7a-e274-4738-9ccb-3dd4dab32928
Alzetani, Aiman
6dc94b31-f7c1-4341-a805-5c8e8d21dbde
Larkin, Oliver
5bde48ce-ef5d-43fa-998d-f38800240514
Haig, Ian
8c3dc208-c92b-4229-ab67-08dda8b45d64
Schneider, Philipp
a810f925-4808-44e4-8a4a-a51586f9d7ad
Lackie, Peter
4afbbe1a-22a6-4ceb-8cad-f3696dc43a7a
Warner, Jane
8571b049-31bb-4a2a-a3c7-4184be20fe25
3 November 2021
Lawson, Matthew
18483732-1f57-4c2c-bccb-0df94f4c71ac
Katsamenis, Orestis L.
8553e7c3-d860-4b7a-a883-abf6c0c4b438
Chatelet, David
6371fd7a-e274-4738-9ccb-3dd4dab32928
Alzetani, Aiman
6dc94b31-f7c1-4341-a805-5c8e8d21dbde
Larkin, Oliver
5bde48ce-ef5d-43fa-998d-f38800240514
Haig, Ian
8c3dc208-c92b-4229-ab67-08dda8b45d64
Schneider, Philipp
a810f925-4808-44e4-8a4a-a51586f9d7ad
Lackie, Peter
4afbbe1a-22a6-4ceb-8cad-f3696dc43a7a
Warner, Jane
8571b049-31bb-4a2a-a3c7-4184be20fe25
Lawson, Matthew, Katsamenis, Orestis L., Chatelet, David, Alzetani, Aiman, Larkin, Oliver, Haig, Ian, Schneider, Philipp, Lackie, Peter and Warner, Jane
(2021)
Immunofluorescence-guided segmentation of three-dimensional features in micro-computed tomography datasets of human lung tissue.
Royal Society Open Science, 8 (11), [211067].
(doi:10.1098/rsos.211067).
Abstract
Micro-computed tomography (µCT) provides non-destructive three-dimensional (3D) imaging of soft tissue microstructures. Specific features in µCT images can be identified using correlated two-dimensional (2D) histology images allowing manual segmentation. However, this is very time-consuming and requires specialist knowledge of the tissue and imaging modalities involved. Using a custom-designed µCT system optimized for imaging unstained formalin-fixed paraffin-embedded soft tissues, we imaged human lung tissue at isotropic voxel sizes less than 10 µm. Tissue sections were stained with haematoxylin and eosin or cytokeratin 18 in columnar airway epithelial cells using immunofluorescence (IF), as an exemplar of this workflow. Novel utilization of tissue autofluorescence allowed automatic alignment of 2D microscopy images to the 3D µCT data using scripted co-registration and automated image warping algorithms. Warped IF images, which were accurately aligned with the µCT datasets, allowed 3D segmentation of immunoreactive tissue microstructures in the human lung. Blood vessels were segmented semi-automatically using the co-registered µCT datasets. Correlating 2D IF and 3D µCT data enables accurate identification, localization and segmentation of features in fixed soft lung tissue. Our novel correlative imaging workflow provides faster and more automated 3D segmentation of µCT datasets. This is applicable to the huge range of formalin-fixed paraffin-embedded tissues held in biobanks and archives.
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Submitted date: 2021
Published date: 3 November 2021
Additional Information:
Funding Information:
The micro-computed tomography scanner (Med-X) technology development, optimized for soft tissue image contrast, was a collaborative effort between Nikon X-Tek Systems Ltd (Tring, UK) and a partnership between the μ-VIS X-ray Imaging Centre at the University of Southampton and the Biomedical Imaging Unit at Southampton General Hospital. M.J.L was partially funded by Nikon XTek Systems Ltd. O.J.L. and I.H. are Nikon X-Tek Systems Ltd employees.
Funding Information:
This work was funded by a PhD studentship to M.J.L. provided by the Medical Research Council (grant no. 1823943) and Nikon X-Tek Systems Ltd, Tring, UK. Acknowledgements
Publisher Copyright:
© 2021 The Authors.
Keywords:
SIFT, blood vessel networks, correlative imaging, histology, registration, warping
Identifiers
Local EPrints ID: 451808
URI: http://eprints.soton.ac.uk/id/eprint/451808
ISSN: 2054-5703
PURE UUID: 8f177a15-cff2-41d8-9484-32973f638266
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Date deposited: 28 Oct 2021 16:33
Last modified: 17 Mar 2024 03:34
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Author:
David Chatelet
Author:
Aiman Alzetani
Author:
Oliver Larkin
Author:
Ian Haig
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