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

J Digit Imaging DOI 10.1007/s10278-017-9966-5 3D Histopathology—a Lung Tissue Segmentation Workflow for Microfocus X-ray-Computed Tomography Scans Lasse Wollatz 1 1 2 & Steven J. Johnston & Peter M. Lackie & Simon J. Cox 1 # The Author(s) 2017. This article is published with open access at Springerlink.com Abstract 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 semiautomatically 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. * Lasse Wollatz 1 Faculty of Engineering and the Environment, University of Southampton, Southampton SO17 1BJ, UK 2 Faculty of Medicine, University of Southampton, Southampton SO17 1BJ, UK Keywords Lung . Image segmentation . Computed tomography . Histopathology . ImageJ . Vascular network Introduction Histopathology provides structural details of tissue samples on a cellular level allowing disease-associated changes to be identified. It offers a key diagnostic tool for fibrotic lung diseases, particularly those that cannot be clearly identified on the basis of patient-computed tomography (CT) or highresolution CT [1–4] and is often regarded as the gold standard [5, 6]. For routine histopathology, surgical biopsies are taken from a patient, providing three-dimensional (3D) tissue samples that are chemically fixed to preserve tissue structure and then embedded in wax to allow for histological sectioning. For diagnostic purposes, sections are stained to identify the overall tissue structure and highlight certain tissue components before analysis under a microscope by a trained histopathologist (see Fig. 1). These microscopy images are increasingly provided through digital scanning of tissue slices. This allows analysis based on the digital image (virtual microscopy). Systems for computer-assisted diagnosis (CAD) used to highlight relevant features or suggest a possible diagnosis are also becoming available [5, 7]. The established methods in histopathology have proven to be critical to the diagnosis of many lung diseases. There are some diseases like idiopathic pulmonary fibrosis, non-specific interstitial pneumonia, or extrinsic allergic alveolitis, where diagnosis is difficult and an agreement between histopathologists is generally only fair to moderate (kappa agreement coefficients ranging from 0.2 to 0.7) [8]. By producing a threedimensional scan of the sample, additional information about the tissue will be gained [9]. Instead of single slices, 100 to 1000 times the number of slices can be viewed, at various slice J Digit Imaging Fig. 1 Current histology workflow (top) and proposed workflow (bottom). As μCT is non-invasive, this can be seen as an additional approach orientations. This aids the identification of rarer structural changes and reveals the degree of heterogeneity in tissue structure. Further benefits include analysis of 3D structures, specifically 3D networks, revealing tissue function as well as interrelationships between objects [9, 10], enabling the application of established stereological methods [11]. Microfocus X-ray-computed tomography (μCT) of soft tissue embedded in wax produces relatively high resolution (~7 μm or better), but low-contrast 3D images. Identification of key features inside these images is challenging but important. Image segmentation describes this process of distinguishing between the areas of interest in an image and the remaining area. Parts of the image, which are not of interest, are commonly removed or areas of different features are marked with different (digital) labels. Segmenting some features such as the walls of the airways and blood vessels manually is effectively impossible; manual segmentation of the lumen, while possible [12], is time-consuming, taking weeks or months per dataset. Automatic or semi-automatic segmentation is therefore required to make this a useable method for research or diagnosis. Many methods [13–17] have been developed for automatic segmentation of images in medicine. Standard approaches such as intensity thresholding [18], watershed algorithms [19] and contour region growing [15] do not have the required sensitivity or specificity when used for noisy low-contrast images like soft tissue. Atlas-based approaches [20] require an ideal model which cannot be created for small and very variable structures like the alveoli. Machine learning approaches can reduce the amount of human input and are flexible enough to deal with low-contrast images. They have previously been used for computer-aided diagnosis in histology and for airway detection in lung patient CT [21, 22]. We present here a workflow that allows semi-automated segmentation of airways and blood vessels and its implementation as LungJ using the free, open-source software ImageJ [23]. The workflow is scalable to large 3D μCT scans and targets lung tissue samples. ‘Materials and Methods’ provides details on sample preparation and image acquisition. The procedural steps of the image-processing workflow itself are presented in ‘The Workflow’. Background and choices made for individual workflow procedures are explained for each step. A short summary of the workflow is provided in the ‘Conclusions’. Materials and Methods A number of steps were required before the digital segmentation: Tissue was prepared for scanning, while scanning conditions were optimized to provide the best possible resolution and contrast for the tissue. Scans also had to be pre-processed before segmentation. Sample Preparation Surgically resected human lung tissue was obtained with written informed consent from patients underg (...truncated)