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21 March 2016 A learning-based, fully automatic liver tumor segmentation pipeline based on sparsely annotated training data
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Abstract
Current fully automatic liver tumor segmentation systems are designed to work on a single CT-image. This hinders these systems from the detection of more complex types of liver tumor. We therefore present a new algorithm for liver tumor segmentation that allows incorporating different CT scans and requires no manual interaction. We derive a liver segmentation with state-of-the-art shape models which are robust to initialization. The tumor segmentation is then achieved by classifying all voxels into healthy or tumorous tissue using Extremely Randomized Trees with an auto-context learning scheme. Using DALSA enables us to learn from only sparse annotations and allows a fast set-up for new image settings. We validate the quality of our algorithm with exemplary segmentation results.
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Michael Goetz, Eric Heim, Keno Maerz, Tobias Norajitra, Mohammadreza Hafezi, Nassim Fard, Arianeb Mehrabi, Max Knoll, Christian Weber, Lena Maier-Hein, and Klaus H. Maier-Hein "A learning-based, fully automatic liver tumor segmentation pipeline based on sparsely annotated training data", Proc. SPIE 9784, Medical Imaging 2016: Image Processing, 97841I (21 March 2016); https://doi.org/10.1117/12.2217655
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