Quantification of pulmonary vessel diameter in low-dose CT images

Abstract

Accurate quantification of vessel diameter in low-dose Computer Tomography (CT) images is important to study pulmonary diseases, in particular for the diagnosis of vascular diseases and the characterization of morphological vascular remodeling in Chronic Obstructive Pulmonary Disease (COPD). In this study, we objectively compare several vessel diameter estimation methods using a physical phantom. Five solid tubes of differing diameters (from 0.898 to 3.980 mm) were embedded in foam, simulating vessels in the lungs. To measure the diameters, we first extracted the vessels using either of two approaches: vessel enhancement using multi-scale Hessian matrix computation, or explicitly segmenting them using intensity threshold. We implemented six methods to quantify the diameter: three estimating diameter as a function of scale used to calculate the Hessian matrix; two calculating equivalent diameter from the crosssection area obtained by thresholding the intensity and vesselness response, respectively; and finally, estimating the diameter of the object using the Full Width Half Maximum (FWHM). We find that the accuracy of frequently used methods estimating vessel diameter from the multi-scale vesselness filter depends on the range and the number of scales used. Moreover, these methods still yield a significant error margin on the challenging estimation of the smallest diameter (on the order or below the size of the CT point spread function). Obviously, the performance of the thresholding-based methods depends on the value of the threshold. Finally, we observe that a simple adaptive thresholding approach can achieve a robust and accurate estimation of the smallest vessels diameter. PURPOSEVessel segmentation is a common task in computer aided processing of data generated by 3D imaging modalities in general, and in chest CT in particular. Quantification of vessel diameter is essential for the correct diagnosis of vascular diseases and also for the study of diseases where vessels may be affected by morphological changes. That is the case of Chronic Obstructive Pulmonary Disease (COPD), where vascular remodeling has been recently identified as one of the associated markers[1].Numerous studies have been published on vessel segmentation[2], [3], but few of them address the issue of vessel quantification. If a binary segmentation is available, a straightforward quantification approach consists of measuring diameters from cross-sectional areas (CSA) extracted from 2D sections[4], thus discarding vessels whose orientation is not perpendicular to the axial plane. To measure the CSA of vessel not perpendicular to the axial, the vessel centerline direction needs to be obtained -either by explicit skeletonization [5] or Hessian-based tensor analysis [6]. The latter is a more elegant approach that estimates the diameter as the scale at which maximum response is given by the smallest eigenvector -the one that corresponds to the direction of the vessel -of the Hessian matrix of the image. To obtain the scale that corresponds to the e...