A learning-based, fully automatic liver tumor segmentation pipeline based on sparsely annotated training data
MICHAEL A. GOETZ, Eric Heim, Keno Maerz, Tobias Norajitra, Mohammadreza Hafezi, Nassim Fard, Arianeb Mehrabi, Max Knoll, Christian Weber, Lena Maier‐Hein, Klaus Hermann Maier-Hein · Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE · 2016
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.