3D Histopathology—a Lung Tissue Segmentation Workflow for Microfocus X-ray-Computed Tomography Scans

• Published in: Journal of digital imaging 2017-12, Vol.30 (6), p.772-781
• Main Authors: Wollatz, Lasse, Johnston, Steven J., Lackie, Peter M., Cox, Simon J.
• Format: Article
• Language: English
• Subjects: Aged; ALGORITHMS; ANIMAL TISSUES; BLOOD VESSELS; Computation; Computed tomography; COMPUTER CODES; COMPUTERIZED TOMOGRAPHY; DATASETS; Diagnostic systems; Histopathology; Humans; Image processing; Image resolution; Image segmentation; Imaging; Imaging, Three-Dimensional - methods; Lung - diagnostic imaging; Lung - pathology; Lung - ultrastructure; Lung Neoplasms - diagnostic imaging; Lung Neoplasms - pathology; Lung Neoplasms - ultrastructure; LUNGS; Male; Medicine; Medicine & Public Health; OPTIMIZATION; Pattern Recognition, Automated - methods; Radiology; RADIOLOGY AND NUCLEAR MEDICINE; RESOLUTION; Source code; Tissue analysis; Tomography, X-Ray Computed - methods; Two dimensional analysis; Workflow; X RADIATION; X-Rays
• ISSN: 0897-1889; 1618-727X; 1618-727X
• DOI: 10.1007/s10278-017-9966-5

Lung histopathology is currently based on the analysis of 2D sections of tissue samples. The use of microfocus X-ray-computed tomography imaging of unstained soft tissue can provide high-resolution 3D image datasets in the range of 2–10 μm 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 interrelationships. 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. Representation of the tubular networks in samples of human lung, building on those segmentations, has been demonstrated using this approach.