Chair for Computer Aided Medical Procedures & Augmented Reality
Lehrstuhl für Informatikanwendungen in der Medizin & Augmented Reality

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M. Feuerstein, T. Kitasaka, K. Mori
Automated Anatomical Likelihood Driven Extraction and Branching Detection of Aortic Arch in 3-D Chest CT
Second International Workshop on Pulmonary Image Analysis, September 2009 (bib)

The extraction and analysis of the aortic arch in chest computed tomography (CT) data can be an important preliminary step for the diagnosis and treatment planning of e.g.~lung cancer. We here present a new method for automatic aortic arch extraction and detection of the main arterial branchings that may serve as segmentation seeds or as landmarks for intra- and interpatient registration of the mediastinum. Our method, which is based on Hough and Euclidean distance transforms and probability weighting, works on both contrast enhanced and non-contrast CT. A comparison to data manually extracted from 40 cases shows its robustness at an acceptable overall runtime.
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