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27 March 2009 Hierarchical parsing and semantic navigation of full body CT data
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Proceedings Volume 7259, Medical Imaging 2009: Image Processing; 725902 (2009)
Event: SPIE Medical Imaging, 2009, Lake Buena Vista (Orlando Area), Florida, United States
Whole body CT scanning is a common diagnosis technique for discovering early signs of metastasis or for differential diagnosis. Automatic parsing and segmentation of multiple organs and semantic navigation inside the body can help the clinician in efficiently obtaining accurate diagnosis. However, dealing with the large amount of data of a full body scan is challenging and techniques are needed for the fast detection and segmentation of organs, e.g., heart, liver, kidneys, bladder, prostate, and spleen, and body landmarks, e.g., bronchial bifurcation, coccyx tip, sternum, lung tips. Solving the problem becomes even more challenging if partial body scans are used, where not all organs are present. We propose a new approach to this problem, in which a network of 1D and 3D landmarks is trained to quickly parse the 3D CT data and estimate which organs and landmarks are present as well as their most probable locations and boundaries. Using this approach, the segmentation of seven organs and detection of 19 body landmarks can be obtained in about 20 seconds with state-of-the-art accuracy and has been validated on 80 CT full or partial body scans.
© (2009) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Sascha Seifert, Adrian Barbu, S. Kevin Zhou, David Liu, Johannes Feulner, Martin Huber, Michael Suehling, Alexander Cavallaro, and Dorin Comaniciu "Hierarchical parsing and semantic navigation of full body CT data", Proc. SPIE 7259, Medical Imaging 2009: Image Processing, 725902 (27 March 2009);

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