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3D histopathology – a lung tissue segmentation workflow for microfocus X-ray computed tomography scans

3D histopathology – a lung tissue segmentation workflow for microfocus X-ray computed tomography scans
3D histopathology – a lung tissue segmentation workflow for microfocus X-ray computed tomography scans
Lung histopathology is currently based on the analysis of 2D sections of tissue samples. The use of microfocus X-ray computed tomography (µCT) imaging of unstained soft tissue can provide high resolution 3D image datasets in the range of 2-10μm without affecting the current diagnostic workflow. Important details of structural features such as the tubular networks of airways and blood vessels are contained in these datasets but are difficult and time consuming to identify by manual image segmentation.
Providing 3D structures permits a better understanding of tissue functions and structural inter-relationships. It also provides a more complete picture of heterogeneous samples. In addition 3D analysis of tissue structure provides the potential for an entirely new level of quantitative measurements of this structure that have previously been based only on extrapolation from 2D sections.
In this paper a workflow for segmenting such 3D images semi-automatically has been created using and extending the ImageJ open-source software and key steps of the workflow have been integrated into a new ImageJ plug-in called LungJ.
Results indicate an improved workflow with a modular organization of steps facilitating the optimization for different sample and scan properties with expert input as required. This allows for incremental and independent optimization of algorithms leading to faster segmentation. Representations of the tubular networks in samples of human lung, building on those segmentations, have been demonstrated using this approach.
Lung, Image segmentation, computed tomography, Histopathology, ImageJ, Vascular Network
0897-1889
772-781
Wollatz, Lasse
7a2e3e37-13e6-47ec-aed8-33078db4c3ff
Johnston, Steven
6b903ec2-7bae-4a56-9c21-eea0a70bfa2b
Lackie, Peter
4afbbe1a-22a6-4ceb-8cad-f3696dc43a7a
Cox, Simon
0e62aaed-24ad-4a74-b996-f606e40e5c55
Wollatz, Lasse
7a2e3e37-13e6-47ec-aed8-33078db4c3ff
Johnston, Steven
6b903ec2-7bae-4a56-9c21-eea0a70bfa2b
Lackie, Peter
4afbbe1a-22a6-4ceb-8cad-f3696dc43a7a
Cox, Simon
0e62aaed-24ad-4a74-b996-f606e40e5c55

Wollatz, Lasse, Johnston, Steven, Lackie, Peter and Cox, Simon (2017) 3D histopathology – a lung tissue segmentation workflow for microfocus X-ray computed tomography scans Journal of Digital Imaging, 30, (6), pp. 772-781. (doi:10.1007/s10278-017-9966-5).

Record type: Article

Abstract

Lung histopathology is currently based on the analysis of 2D sections of tissue samples. The use of microfocus X-ray computed tomography (µCT) imaging of unstained soft tissue can provide high resolution 3D image datasets in the range of 2-10μm without affecting the current diagnostic workflow. Important details of structural features such as the tubular networks of airways and blood vessels are contained in these datasets but are difficult and time consuming to identify by manual image segmentation.
Providing 3D structures permits a better understanding of tissue functions and structural inter-relationships. It also provides a more complete picture of heterogeneous samples. In addition 3D analysis of tissue structure provides the potential for an entirely new level of quantitative measurements of this structure that have previously been based only on extrapolation from 2D sections.
In this paper a workflow for segmenting such 3D images semi-automatically has been created using and extending the ImageJ open-source software and key steps of the workflow have been integrated into a new ImageJ plug-in called LungJ.
Results indicate an improved workflow with a modular organization of steps facilitating the optimization for different sample and scan properties with expert input as required. This allows for incremental and independent optimization of algorithms leading to faster segmentation. Representations of the tubular networks in samples of human lung, building on those segmentations, have been demonstrated using this approach.

Text 3D Histopathology – A Lung Tissue Segmentation Workflow for Microfocus X-ray Computed Tomography Scans - Accepted Manuscript
Restricted to Repository staff only until 24 March 2018.
Text s10278-017-9966-5 - Version of Record
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More information

Submitted date: 16 December 2016
Accepted/In Press date: 12 March 2017
e-pub ahead of print date: 24 March 2017
Published date: December 2017
Keywords: Lung, Image segmentation, computed tomography, Histopathology, ImageJ, Vascular Network
Organisations: Computational Engineering & Design Group, Clinical & Experimental Sciences, Education Hub

Identifiers

Local EPrints ID: 406469
URI: http://eprints.soton.ac.uk/id/eprint/406469
ISSN: 0897-1889
PURE UUID: cf3a7d9d-25fd-454c-ac57-e148b8110ca9
ORCID for Lasse Wollatz: ORCID iD orcid.org/0000-0002-8761-7884
ORCID for Steven Johnston: ORCID iD orcid.org/0000-0003-3864-7072
ORCID for Peter Lackie: ORCID iD orcid.org/0000-0001-7138-3764

Catalogue record

Date deposited: 18 Mar 2017 02:05
Last modified: 13 Nov 2017 17:31

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