3D statistical shape models incorporating 3D random forest regression voting for robust CT liver segmentation

Tobias Norajitra; Hans-Peter Meinzer; Klaus H. Maier-Hein

doi:10.1117/12.2082909

20 March 2015 3D statistical shape models incorporating 3D random forest regression voting for robust CT liver segmentation

Tobias Norajitra, Hans-Peter Meinzer, Klaus H. Maier-Hein

Author Affiliations +

Proceedings Volume 9414, Medical Imaging 2015: Computer-Aided Diagnosis; 941406 (2015) https://doi.org/10.1117/12.2082909
Event: SPIE Medical Imaging, 2015, Orlando, Florida, United States

Abstract

During image segmentation, 3D Statistical Shape Models (SSM) usually conduct a limited search for target landmarks within one-dimensional search profiles perpendicular to the model surface. In addition, landmark appearance is modeled only locally based on linear profiles and weak learners, altogether leading to segmentation errors from landmark ambiguities and limited search coverage. We present a new method for 3D SSM segmentation based on 3D Random Forest Regression Voting. For each surface landmark, a Random Regression Forest is trained that learns a 3D spatial displacement function between the according reference landmark and a set of surrounding sample points, based on an infinite set of non-local randomized 3D Haar-like features. Landmark search is then conducted omni-directionally within 3D search spaces, where voxelwise forest predictions on landmark position contribute to a common voting map which reflects the overall position estimate. Segmentation experiments were conducted on a set of 45 CT volumes of the human liver, of which 40 images were randomly chosen for training and 5 for testing. Without parameter optimization, using a simple candidate selection and a single resolution approach, excellent results were achieved, while faster convergence and better concavity segmentation were observed, altogether underlining the potential of our approach in terms of increased robustness from distinct landmark detection and from better search coverage.

Citation Download Citation

Tobias Norajitra, Hans-Peter Meinzer, and Klaus H. Maier-Hein "3D statistical shape models incorporating 3D random forest regression voting for robust CT liver segmentation", Proc. SPIE 9414, Medical Imaging 2015: Computer-Aided Diagnosis, 941406 (20 March 2015); https://doi.org/10.1117/12.2082909

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