New Tools for Computer Assistance in Thoracic CT. Part 1. Functional Analysis of Lungs, Lung Lobes, and Bronchopulmonary Segments

Abstract

Owing to the rapid development of scanner technology, thoracic computed tomography (CT) offers new possibilities but also faces enormous challenges with respect to the quality of computer-assisted diagnosis and therapy planning. In the framework of the Virtual Institute for Computer Assistance in Clinical Radiology cooperative research project, a prototypical software application was developed to assist the radiologist in functional analysis of thoracic CT data. By identifying the anatomic compartments of the lungs, the software application enables assessment of established functional CT parameters for each individual lung, pulmonary lobe, and pulmonary segment. Such region-based assessment allows a more localized diagnosis of lung diseases such as emphysema and more accurate estimation of regional lung function from CT data. With close cooperation between computer scientists and radiologists, the software application was tested and optimized to achieve a high degree of usability. Several clinical studies were carried out, the results of which indicated that the software application improves quantification in diagnosis, therapy planning, and therapy monitoring with respect to accuracy and time required.

Interstitial lung disease associated with collagen vascular disorders: disease quantification using a computer-aided diagnosis tool

“…The algorithm has been described previously [4] and is characterized by an initial segmentation of the airways, based on a regiongrowing method, that is followed by airway analysis in order to obtain a graphic representation of the bronchial tree, including information about direction, diameter, and length of each branch [6,7]. The bronchi belonging to each pulmonary segment are identified automatically.…”

“…Based on the bronchial tree, a preliminary mask of the lung parenchyma is created using a conventional regiongrowing method with a fixed threshold interval (−1,024 to −400 HU) [4]. Subsequently, a marker-based 3D watershed transform is used that is confined to the area of the preliminary lung masks in order to obtain separate masks for each lung [4,8].…”

“…Subsequently, a marker-based 3D watershed transform is used that is confined to the area of the preliminary lung masks in order to obtain separate masks for each lung [4,8]. The segmentation of the pulmonary vasculature is restricted to the area defined by the previously computed lung masks and is performed using a conventional 3D region-growing algorithm, thereby creating a superset of all larger pulmonary vessels.…”

“…For this purpose, a Euclidean distance transform is used, and lobar areas are obtained from the preprocessed image using a multidimensional interactive watershed transform [9]. These region identification steps enable extraction of various CT parameters [4]. Initially developed for quantification of pulmonary emphysema, the software allows extraction of the volume, mean density, pixel index, bulla index, and emphysema type on a lobar or segmental basis [4].…”

“…The majority of quantitative analysis methods involve the application of thresholding methods to the segmented pulmonary parenchyma in order to extract regions with attenuation values either above or below a user-defined threshold criterion [3]. This approach may potentially allow a precise, time-efficient, and reproducible quantification of the diseased pulmonary parenchyma, as it requires only minimal user interaction for definition of a threshold value [3,4]. Hence, the aim of this study was to evaluate the performance of a CAD prototype software for quantification of disease extent in patients with ILD associated with CVD and its correlation with physiological impairment, in comparison with reader-based disease quantification.…”