TY - GEN
T1 - 2-Step robust vertebra segmentation
AU - Courbot, Jean Baptiste
AU - Rust, Edmond
AU - Monfrini, Emmanuel
AU - Collet, Christophe
N1 - Publisher Copyright:
© 2015 IEEE.
PY - 2015/12/28
Y1 - 2015/12/28
N2 - Knowledge of vertebra location, shape and orientation is crucial in many medical applications such as orthopedics or interventional procedures. The wide range of shapes, joint alterations and pathological cases encountered in an aging population makes automatic segmentation sometimes challenging. This paper presents a new automated vertebra segmentation method for 3D CT data which tackles these problems. This method has two consecutive main steps: first a new coarse-to-fine method produces a coarse shape of the vertebra, then a Hidden Markov Chain (HMC) segmentation using a specific volume transformation refine the segmentation. No shape prior is used thus allowing most frequent non-standard and pathological cases handling. We experiment this method on a set of standard vertebrae and on non-standard cases as encountered in daily practice. After expert validation, we show that our method is robust to shape and luminance changes, and provides correct segmentation for pathological cases.
AB - Knowledge of vertebra location, shape and orientation is crucial in many medical applications such as orthopedics or interventional procedures. The wide range of shapes, joint alterations and pathological cases encountered in an aging population makes automatic segmentation sometimes challenging. This paper presents a new automated vertebra segmentation method for 3D CT data which tackles these problems. This method has two consecutive main steps: first a new coarse-to-fine method produces a coarse shape of the vertebra, then a Hidden Markov Chain (HMC) segmentation using a specific volume transformation refine the segmentation. No shape prior is used thus allowing most frequent non-standard and pathological cases handling. We experiment this method on a set of standard vertebrae and on non-standard cases as encountered in daily practice. After expert validation, we show that our method is robust to shape and luminance changes, and provides correct segmentation for pathological cases.
KW - Automatic vertebra segmentation
KW - Clinical imagery
KW - Coarse-to-fine modeling
KW - Hidden Markov Chains
KW - SLIC Clustering
UR - https://www.scopus.com/pages/publications/84963807811
U2 - 10.1109/IPTA.2015.7367118
DO - 10.1109/IPTA.2015.7367118
M3 - Conference contribution
AN - SCOPUS:84963807811
T3 - 5th International Conference on Image Processing, Theory, Tools and Applications 2015, IPTA 2015
SP - 157
EP - 162
BT - 5th International Conference on Image Processing, Theory, Tools and Applications 2015, IPTA 2015
A2 - Jennane, Rachid
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 5th International Conference on Image Processing, Theory, Tools and Applications 2015, IPTA 2015
Y2 - 10 November 2015 through 13 November 2015
ER -