The VESSEL12 (VESsel SEgmentation in the Lung) challenge objectively compares the performance of different algorithms to identify vessels in thoracic computed tomography (CT) scans. Vessel segmentation is fundamental in computer aided processing of data generated by 3D imaging modalities. As manual vessel segmentation is prohibitively time consuming, any real world application requires some form of automation. Several approaches exist for automated vessel segmentation, but judging their relative merits is difficult due to a lack of standardized evaluation. We present an annotated reference dataset containing 20 CT scans and propose nine categories to perform a comprehensive evaluation of vessel segmentation algorithms from both academia and industry. Twenty algorithms participated in the VESSEL12 challenge, held at International Symposium on Biomedical Imaging (ISBI) 2012. All results have been published at the VESSEL12 website http://vessel12.grand-challenge.org. The challenge remains ongoing and open to new participants. Our three contributions are: (1) an annotated reference dataset available online for evaluation of new algorithms; (2) a quantitative scoring system for objective comparison of algorithms; and (3) performance analysis of the strengths and weaknesses of the various vessel segmentation methods in the presence of various lung diseases.

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This paper describes a fully automatic simultaneous lung vessel and airway enhancement filter. The approach consists of a Frangi-based multiscale vessel enhancement filtering specifically designed for lung vessel and airway detection, where arteries and veins have high contrast with respect to the lung parenchyma, and airway walls are hollow tubular structures with a non negative response using the classical Frangi's filter. The features extracted from the Hessian matrix are used to detect centerlines and approximate walls of airways, decreasing the filter response in those areas by applying a penalty function to the vesselness measure. We validate the segmentation method in 20 CT scans with different pathological states within the VES SEL 12 challenge framework. Results indicate that our approach obtains good results, decreasing the number of false positives in airway walls.

In this study, we quantitatively characterize lung airway remodeling caused by smoking-related emphysema and Chronic Obstructive Pulmonary Disease (COPD), in low-dose CT scans. To that end, we established three groups of individuals: subjects with COPD (n=35), subjects with emphysema (n=38) and healthy smokers (n=28). All individuals underwent a low-dose CT scan, and the images were analyzed as described next. First the lung airways were segmented using a fast marching method and labeled according to its generation. Along each airway segment, cross-section images were resampled orthogonal to the airway axis. Next, 128 rays were cast from the center of the airway lumen in each crosssection slice. Finally, we used an integral-based method to measure lumen radius, wall thickness, mean wall percentage and mean peak wall attenuation on every cast ray. Our analysis shows that both the mean global wall thickness and the lumen radius of the airways of both COPD and emphysema groups were significantly different from those of the healthy group. In addition, the wall thickness starts changing at the 3 rd airway generation in COPD patients compared with emphysema patients, who suffer the first significant changes starting in the 2 nd generation. In conclusion, it is shown that airway remodeling happens in individuals suffering from either COPD or emphysema, with some local difference between both groups, and that we are able to detect and accurately quantify this process using images of low-dose CT scans.

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...

We present a probability model for lung airways in computed tomography (CT) images. Lung airways are tubular structures that display specific features, such as low intensity and proximity to vessels and bronchial walls. From these features, the posterior probability for the airway feature space was computed using a Bayesian model based on 20 CT images from subjects with different degrees of Chronic Obstructive Pulmonary Disease (COPD). The likelihood probability was modeled using both a Gaussian distribution and a nonparametric kernel density estimation method. After exhaustive feature selection, good specificity and sensitivity were achieved in a cross-validation study for both the Gaussian (0.83, 0.87) and the nonparametric method (0.79, 0.89). The model generalizes well when trained using images from a late stage COPD group. This probability model may facilitate airway extraction and quantitative assessment of lung diseases, which is useful in many clinical and research settings.

We present an automatic method to track individual nodule progression in a lung cancer mouse model. Fourteen A/J mice received an intraperitoneal injection of urethane. Respiratory-gated micro-CT images of the lungs were taken 8, 22, and 37 weeks after injection, at which 195, 585 and 636 nodules were manually detected. The three images from every animal were registered and their nodules matched with average accuracy of 97.2%. All nodules detected at week 8 were then tracked until week 37, and volumetrically segmented to characterize the growth rate and doubling rate. Our framework is able to segment 92.9% of all nodules, ranging from the earliest stage (0.2mm) to advanced stage where nodule segmentation becomes challenging due to complex anatomy and nodule overlap. In conclusion, we showed the utility of the proposed framework to facilitate further research in pre-clinical lung cancer model.