Pulmonary Vascular Tree Segmentation from Contrast-Enhanced CT Images

We present a pulmonary vessel segmentation algorithm, which is fast, fully automatic and robust. It uses a coarse segmentation of the airway tree and a left and right lung labeled volume to restrict a vessel enhancement filter, based on an offset medialness function, to the lungs. We show the application of our algorithm on contrast-enhanced CT images, where we derive a clinical parameter to detect pulmonary hypertension (PH) in patients. Results on a dataset of 24 patients show that quantitative indices derived from the segmentation are applicable to distinguish patients with and without PH. Further work-in-progress results are shown on the VESSEL12 challenge dataset, which is composed of non-contrast-enhanced scans, where we range in the midfield of participating contestants.Comment: Part of the OAGM/AAPR 2013 proceedings (1304.1876

Comparing algorithms for automated vessel segmentation in computed tomography scans of the lung: the VESSEL12 study

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.Rudyanto, RD.; Kerkstra, S.; Van Rikxoort, EM.; Fetita, C.; Brillet, P.; Lefevre, C.; Xue, W.... (2014). Comparing algorithms for automated vessel segmentation in computed tomography scans of the lung: the VESSEL12 study. Medical Image Analysis. 18(7):1217-1232. doi:10.1016/j.media.2014.07.003S1217123218

Quantitative CT-derived vessel metrics in idiopathic pulmonary fibrosis: A structure function study

BACKGROUND AND OBJECTIVE: To investigate whether quantitative lung vessel morphology determined by a new fully-automated algorithm is associated with functional indices in idiopathic pulmonary fibrosis (IPF). METHODS: 152 IPF patients had vessel volume, density, tortuosity and heterogeneity quantified from CT images by a fully-automated algorithm. Separate quantitation of vessel metrics in pulmonary arteries and veins was performed in 106 patients. Results were evaluated against readouts from lung function tests. RESULTS: Normalized vessel volume expressed as a percentage of total lung volume was moderately correlated with functional indices on univariable linear regression analysis: forced vital capacity (R2=0.27, p<1x10-6); diffusion capacity of carbon monoxide (DLco; R2=0.12, p=3x10-5); total lung capacity (TLC; R2=0.45, p<1x10-6); composite physiologic index (CPI; R2=0.28, p<1x10-6). Normalized vessel volume was correlated with vessel density but not with vessel heterogeneity. Quantitatively-derived vessel metrics (and artery and vein subdivision scores) were not significantly linked with the transfer factor for carbon monoxide (Kco), and only weakly with DLco. On multivariable linear regression analysis, normalized vessel volume and vessel heterogeneity were independently linked with DLco, TLC and CPI indicating that they capture different aspects of lung damage. Artery-vein separation provided no additional information beyond that captured in the whole vasculature. CONCLUSION: Our study confirms previous observations of links between vessel volume and functional measures of disease severity in IPF using a new vessel-quantitation tool. Additionally, the new tool shows independent linkages of normalized vessel volume and vessel heterogeneity with functional indices. Quantitative vessel metrics do not appear to reflect vasculopathic damage in IPF