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Contains fulltext : 173114.pdf (Publisher’s version ) (Open Access) We propose a novel method to improve airway segmentation in thoracic computed tomography (CT) by detecting and removing leaks. Leak detection is formulated as a classification problem, in which a convolutional network (ConvNet) is trained in a supervised fashion to perform the classification task. In order to increase the segmented airway tree length, we take advantage of the fact that multiple segmentations can be extracted from a given airway segmentation algorithm by varying the parameters that influence the tree length and the amount of leaks. We propose a strategy in which the combination of these segmentations after removing leaks can increase the airway tree length while limiting the amount of leaks. This strategy therefore largely circumvents the need for parameter fine-tuning of a given airway segmentation algorithm. The ConvNet was trained and evaluated using a subset of inspiratory thoracic CT scans taken from the COPDGene study. Our method was validated on a separate independent set of the EXACT'09 challenge. We show that our method significantly improves the quality of a given leaky airway segmentation, achieving a higher sensitivity at a low false-positive rate compared to all the state-of-the-art methods that entered in EXACT09, and approaching the performance of the combination of all of them.

Contains fulltext : 167183.pdf (Publisher’s version ) (Open Access) We present a method for automatic separation and classification of pulmonary arteries and veins in computed tomography. Our method takes advantage of local information to separate segmented vessels, and global information to perform the artery-vein classification. Given a vessel segmentation, a geometric graph is constructed that represents both the topology and the spatial distribution of the vessels. All nodes in the geometric graph where arteries and veins are potentially merged are identified based on graph pruning and individual branching patterns. At the identified nodes, the graph is split into subgraphs that each contain only arteries or veins. Based on the anatomical information that arteries and veins approach a common alveolar sag, an arterial subgraph is expected to be intertwined with a venous subgraph in the periphery of the lung. This relationship is quantified using periphery matching and is used to group subgraphs of the same artery-vein class. Artery-vein classification is performed on these grouped subgraphs based on the volumetric difference between arteries and veins. A quantitative evaluation was performed on 55 publicly available non-contrast CT scans. In all scans, two observers manually annotated randomly selected vessels as artery or vein. Our method was able to separate and classify arteries and veins with a median accuracy of 89%, closely approximating the inter-observer agreement. All CT scans used in this study, including all results of our system and all manual annotations, are publicly available at "http://www.w3.org/1999/xlink">http://arteryvein.grand-challenge.org".

The presence of collateral blood flow is found to be a strong predictor of patient outcome after acute ischemic stroke. Collateral blood flow is defined as an alternative way to provide oxygenated blood to ischemic cerebral tissue. Assessment of collateral blood supply is currently performed by visual inspection of a Computed Tomography Angiogram (CTA) which introduces inter-observer variability and depends on the grading scale. Furthermore, variations in the arterial contrast arrival time may lead to underestimation of collateral blood supply in a CTA which exerts a negative influence on the prediction of patient outcome. In this study, the feasibility of a Computer-aided Diagnosis system is investigated capable of objectively predicting patient outcome. We present a novel automatic method for quantitative assessment of cerebral hypoperfusion in timing-invariant (i.e. delay insensitive) CTA (TI-CTA). The proposed Vessel Density Symmetry algorithm automatically generates descriptive maps based on hemispheric asymmetry of blood vessels. Intensity and symmetry based features are extracted from these descriptive maps and subjected to a best-first-search feature selection. Linear Discriminant Analysis is performed to combine selected features into a likelihood of good patient outcome. Receiver operating characteristic (ROC) analysis is conducted to evaluate the diagnostic performance of the CAD by leave-one- patient-out cross validation. A Positive Predicting Value of 1 was obtained at a sensitivity of 25% with an area under the ROC-curve of 0.86. The results show that the CAD is feasible to objectively predict patient outcome. The presented CAD could make an important contribution to acute ischemic stroke diagnosis and treatment.