Robust extraction of the aorta and pulmonary artery from 3D MDCT image data

Accurate definition of the aorta and pulmonary artery from three-dimensional (3D) multi-detector CT (MDCT) images is important for pulmonary applications. This work presents robust methods for defining the aorta and pulmonary artery in the central chest. The methods work on both contrast enhanced and no-contrast 3D MDCT image data. The automatic methods use a common approach employing model fitting and selection and adaptive refinement. During the occasional event that more precise vascular extraction is desired or the method fails, we also have an alternate semi-automatic fail-safe method. The semi-automatic method extracts the vasculature by extending the medial axes into a user-guided direction. A ground-truth study over a series of 40 human 3D MDCT images demonstrates the efficacy, accuracy, robustness, and efficiency of the methods.

[1]  Olivier Ecabert,et al.  Segmentation of the heart and major vascular structures in cardiovascular CT images , 2008, SPIE Medical Imaging.

[2]  Max A. Viergever,et al.  Multi-Atlas-Based Segmentation With Local Decision Fusion—Application to Cardiac and Aortic Segmentation in CT Scans , 2009, IEEE Transactions on Medical Imaging.

[3]  Richard A. Robb,et al.  Validation of semi-automatic segmentation of the left atrium , 2008, SPIE Medical Imaging.

[4]  Ronald M. Summers,et al.  Pulmonary artery segmentation and quantification in sickle cell associated pulmonary hypertension , 2008, SPIE Medical Imaging.

[5]  Jun-ichiro Toriwaki,et al.  New algorithms for euclidean distance transformation of an n-dimensional digitized picture with applications , 1994, Pattern Recognit..

[6]  Bernard Gosselin,et al.  Pulmonary Arteries Segmentation and Feature Extraction through Slice Marching , 2003 .

[7]  William E. Higgins,et al.  Method for extracting the aorta from 3D CT images , 2007, SPIE Medical Imaging.

[8]  Takayuki Kitasaka,et al.  A method for automated extraction of aorta and pulmonary artery in the mediastinum using medial line models from 3D chest X-ray CT images without contrast materials , 2002, Object recognition supported by user interaction for service robots.

[9]  Kunio Doi,et al.  Computerized detection of pulmonary embolism in spiral CT angiography based on volumetric image analysis , 2002, IEEE Transactions on Medical Imaging.

[10]  Robert K. Colwell,et al.  A new statistical approach for assessing similarity of species composition with incidence and abundance data , 2004 .

[11]  Takayuki Kitasaka,et al.  Automated extraction of aorta and pulmonary artery in mediastinum from 3D chest x-ray CT images without contrast medium , 2002, SPIE Medical Imaging.

[12]  Larry S. Davis,et al.  A new class of edge-preserving smoothing filters , 1987, Pattern Recognit. Lett..

[13]  T. Kitasaka,et al.  Automated Anatomical Likelihood Driven Extraction and Branching Detection of Aortic Arch in 3-D Chest CT , 2009 .

[14]  William E. Higgins,et al.  Extraction and visualization of the central chest lymph-node stations , 2008, SPIE Medical Imaging.

[15]  William E. Higgins,et al.  Planning and Visualization of Accessible, Safe Bronchoscopy Routes: Application to Central-Chest Lymph Node Biopsy. , 2009, ATS 2009.

[16]  William E. Higgins,et al.  Interactive segmentation based on the live wire for 3D CT chest image analysis , 2007, International Journal of Computer Assisted Radiology and Surgery.

[17]  A. Fenster,et al.  Evaluation of Segmentation algorithms for Medical Imaging , 2005, 2005 IEEE Engineering in Medicine and Biology 27th Annual Conference.

[18]  T. McLoud,et al.  CT depiction of regional nodal stations for lung cancer staging. , 2000, AJR. American journal of roentgenology.

[19]  William E. Higgins,et al.  System for definition of the central-chest vasculature , 2009, Medical Imaging.