TY - GEN
T1 - Detailed vertebral segmentation using part-based decomposition and conditional shape models
AU - Pereañez, Marco
AU - Lekadir, Karim
AU - Hoogendoorn, Corné
AU - Castro-Mateos, Isaac
AU - Frangi, Alejandro
N1 - Publisher Copyright:
© Springer International Publishing Switzerland 2015.
PY - 2015
Y1 - 2015
N2 - With the advances in minimal invasive surgical procedures, accurate and detailed extraction of the vertebral boundaries is required. In practice, this is a difficult challenge due to the highly complex geometry of the vertebrae, in particular at the processes. This paper presents a statistical modeling approach for detailed vertebral segmentation based on part decomposition and conditional models. To this end, a Vononoi decomposition approach is employed to ensure that each of the main subparts the vertebrae is identified in the subdivision. The obtained shape constraints are effectively relaxed, allowing for an improved encoding of the fine details and shape variability at all the regions of the vertebrae. Subsequently, in order to maintain the statistical coherence of the ensemble, conditional models are used to model the statistical inter-relationships between the different subparts. For shape reconstruction and segmentation, a robust model fitting procedure is introduced to exclude outlying inter-part relationships in the estimation of the shape parameters. The experimental results based on a database of 30 CT scans show significant improvement in accuracy with respect to the state-of-the-art and the potential of the proposed technique for detailed vertebral modeling.
AB - With the advances in minimal invasive surgical procedures, accurate and detailed extraction of the vertebral boundaries is required. In practice, this is a difficult challenge due to the highly complex geometry of the vertebrae, in particular at the processes. This paper presents a statistical modeling approach for detailed vertebral segmentation based on part decomposition and conditional models. To this end, a Vononoi decomposition approach is employed to ensure that each of the main subparts the vertebrae is identified in the subdivision. The obtained shape constraints are effectively relaxed, allowing for an improved encoding of the fine details and shape variability at all the regions of the vertebrae. Subsequently, in order to maintain the statistical coherence of the ensemble, conditional models are used to model the statistical inter-relationships between the different subparts. For shape reconstruction and segmentation, a robust model fitting procedure is introduced to exclude outlying inter-part relationships in the estimation of the shape parameters. The experimental results based on a database of 30 CT scans show significant improvement in accuracy with respect to the state-of-the-art and the potential of the proposed technique for detailed vertebral modeling.
UR - http://www.scopus.com/inward/record.url?scp=84922884906&partnerID=8YFLogxK
U2 - 10.1007/978-3-319-14148-0_9
DO - 10.1007/978-3-319-14148-0_9
M3 - Conference contribution
AN - SCOPUS:84922884906
T3 - Lecture Notes in Engineering and Computer Science
SP - 95
EP - 103
BT - Recent Advances in Computational Methods and Clinical Applications for Spine Imaging
A2 - Yao, Jianhua
A2 - Glocker, Ben
A2 - Klinder, Tobias
A2 - Li, Shuo
A2 - Li, Shuo
PB - Newswood Ltd
T2 - 2nd Workshop on Computational Methods and Clinical Applications for Spine Imaging, CSI 2014 held in conjunction with MICCAI 2014
Y2 - 14 September 2014 through 14 September 2014
ER -