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12 March 2010 Adaptive model based pulmonary artery segmentation in 3D chest CT
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Abstract
The extraction and analysis of the pulmonary artery in computed tomography (CT) of the chest can be an important, but time-consuming step for the diagnosis and treatment of lung disease, in particular in non-contrast data, where the pulmonary artery has low contrast and frequently merges with adjacent tissue of similar intensity. We here present a new method for the automatic segmentation of the pulmonary artery based on an adaptive model, Hough and Euclidean distance transforms, and spline fitting, which works equally well on non-contrast and contrast enhanced data. An evaluation on 40 patient data sets and a comparison to manual segmentations in terms of Jaccard index, sensitivity, specificity, and minimum mean distance shows its overall robustness.
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Marco Feuerstein, Takayuki Kitasaka, and Kensaku Mori "Adaptive model based pulmonary artery segmentation in 3D chest CT", Proc. SPIE 7623, Medical Imaging 2010: Image Processing, 76234S (12 March 2010); https://doi.org/10.1117/12.843750
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