Automatic pulmonary vessel segmentation in 3D computed tomographic pulmonary angiographic (CTPA) images

Chuan Zhou; Heang-Ping Chan; Lubomir M. Hadjiiski; Smita Patel; Philip N. Cascade; Berkman Sahiner; Jun Wei; Jun Ge; Ella A. Kazerooni

doi:10.1117/12.655343

15 March 2006 Automatic pulmonary vessel segmentation in 3D computed tomographic pulmonary angiographic (CTPA) images

Chuan Zhou, Heang-Ping Chan, Lubomir M. Hadjiiski, Smita Patel, Philip N. Cascade, Berkman Sahiner, Jun Wei, Jun Ge, Ella A. Kazerooni

Author Affiliations +

Proceedings Volume 6144, Medical Imaging 2006: Image Processing; 61444Q (2006) https://doi.org/10.1117/12.655343
Event: Medical Imaging, 2006, San Diego, California, United States

Abstract

Automatic and accurate segmentation of the pulmonary vessels in 3D computed tomographic angiographic images (CTPA) is an essential step for computerized detection of pulmonary embolism (PE) because PEs only occur inside the pulmonary arteries. We are developing an automated method to segment the pulmonary vessels in 3D CTPA images. The lung region is first extracted using thresholding and morphological operations. 3D multiscale filters in combination with a newly developed response function derived from the eigenvalues of Hessian matrices are used to enhance all vascular structures including the vessel bifurcations and suppress non-vessel structures such as the lymphoid tissues surrounding the vessels. At each scale, a volume of interest (VOI) containing the response function value at each voxel is defined. The voxels with a high response indicate that there is an enhanced vessel whose size matches the given filter scale. A hierarchical expectation-maximization (EM) estimation is then applied to the VOI to segment the vessel by extracting the high response voxels at this single scale. The vessel tree is finally reconstructed by combining the segmented vessels at all scales based on a "connected component" analysis. Two experienced thoracic radiologists provided the gold standard of pulmonary arteries by manually tracking the arterial tree and marking the center of the vessels using a computer graphical user interface. Two CTPA cases containing PEs were used to evaluate the performance. One of these two cases also contained other lung diseases. The accuracy of vessel tree segmentation was evaluated by the percentage of the "gold standard" vessel center points overlapping with the segmented vessels. The result shows that 97.3% (1868/1920) and 92.0% (2277/2476) of the manually marked center points overlapped with the segmented vessels for the cases without and with other lung disease, respectively. The results demonstrate that vessel segmentation using our method is not degraded by PE occlusion and the vessels can be accurately extracted.

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Chuan Zhou, Heang-Ping Chan, Lubomir M. Hadjiiski, Smita Patel, Philip N. Cascade, Berkman Sahiner, Jun Wei, Jun Ge, and Ella A. Kazerooni "Automatic pulmonary vessel segmentation in 3D computed tomographic pulmonary angiographic (CTPA) images", Proc. SPIE 6144, Medical Imaging 2006: Image Processing, 61444Q (15 March 2006); https://doi.org/10.1117/12.655343

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