3D histopathology – a lung tissue segmentation workflow for microfocus X-ray computed tomography scans
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 (doi:10.1007/s10278-017-9966-5).
PDF 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.
Available under License University of Southampton Accepted Manuscript Licence.
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.
|Digital Object Identifier (DOI):||doi:10.1007/s10278-017-9966-5|
|Keywords:||Lung, Image segmentation, computed tomography, Histopathology, ImageJ, Vascular Network|
|Organisations:||Computational Engineering & Design Group, Clinical & Experimental Sciences, Education Hub|
|Date Deposited:||18 Mar 2017 02:05|
|Last Modified:||16 Apr 2017 17:03|
|Further Information:||Google Scholar|
|RDF:||RDF+N-Triples, RDF+N3, RDF+XML, Browse.|
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