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Proceedings Paper

A learning-based, fully automatic liver tumor segmentation pipeline based on sparsely annotated training data
Author(s): Michael Goetz; Eric Heim; Keno Maerz; Tobias Norajitra; Mohammadreza Hafezi; Nassim Fard; Arianeb Mehrabi; Max Knoll; Christian Weber; Lena Maier-Hein; Klaus H. Maier-Hein
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Paper 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.

Paper Details

Date Published: 21 March 2016
PDF: 6 pages
Proc. SPIE 9784, Medical Imaging 2016: Image Processing, 97841I (21 March 2016); doi: 10.1117/12.2217655
Show Author Affiliations
Michael Goetz, Deutsches Krebsforschungszentrum (Germany)
Eric Heim, Deutsches Krebsforschungszentrum (Germany)
Keno Maerz, Deutsches Krebsforschungszentrum (Germany)
Tobias Norajitra, Deutsches Krebsforschungszentrum (Germany)
Mohammadreza Hafezi, Univ. Heidelberg (Germany)
Nassim Fard, Univ. Heidelberg (Germany)
Arianeb Mehrabi, Univ. Heidelberg (Germany)
Max Knoll, Deutsches Krebsforschungszentrum (Germany)
Christian Weber, Deutsches Krebsforschungszentrum (Germany)
Lena Maier-Hein, Deutsches Krebsforschungszentrum (Germany)
Klaus H. Maier-Hein, Deutsches Krebsforschungszentrum (Germany)

Published in SPIE Proceedings Vol. 9784:
Medical Imaging 2016: Image Processing
Martin A. Styner; Elsa D. Angelini, Editor(s)

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