A learning-based, fully automatic liver tumor segmentation pipeline based on sparsely annotated training data

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

doi:10.1117/12.2217655

21 March 2016 A learning-based, fully automatic liver tumor segmentation pipeline based on sparsely annotated training data

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

Author Affiliations +

Proceedings Volume 9784, Medical Imaging 2016: Image Processing; 97841I (2016) https://doi.org/10.1117/12.2217655
Event: SPIE Medical Imaging, 2016, San Diego, California, United States

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.

Citation Download Citation

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

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available