TY - GEN
T1 - Statistical shape modeling using partial least squares
T2 - 6th International Workshop on Statistical Atlases and Computational Models of the Heart, STACOM 2015
AU - Lekadir, Karim
AU - Albà, Xènia
AU - Pereañez, Marco
AU - Frangi, Alejandro F.
N1 - Publisher Copyright:
© Springer International Publishing Switzerland 2016.
PY - 2016/1/9
Y1 - 2016/1/9
N2 - Statistical shape modeling (SSM) is a widely popular framework in cardiac image analysis, especially for image segmentation and computer-aided diagnosis. However, the conventional PCA-based models produce new axes of variation which are statistically motivated but thus are not necessarily clinically meaningful. In this paper, we propose an alternative method for statistical decomposition of the shape variability based on partial least squares (PLS). With this method, the model construction is achieved such that it is constrained by the specific clinical question of interest (e.g., estimation of disease state). To achieve this, instead of deriving modes of variation in the directions of maximal variation as in PCA, PLS searches for new axes of variation that correlate most with some output clinical response variables such as diagnostic labels, leading to a decomposition that is anatomically and clinically more meaningful. The validation carried out with 200 cases from the Cardiac Atlas Project database as part of the MICCAI 2015 challenge on SSM, including healthy and infarcted left ventricles, shows the strength of the proposed PLS-based statistical shape model, with 98% prediction accuracy.
AB - Statistical shape modeling (SSM) is a widely popular framework in cardiac image analysis, especially for image segmentation and computer-aided diagnosis. However, the conventional PCA-based models produce new axes of variation which are statistically motivated but thus are not necessarily clinically meaningful. In this paper, we propose an alternative method for statistical decomposition of the shape variability based on partial least squares (PLS). With this method, the model construction is achieved such that it is constrained by the specific clinical question of interest (e.g., estimation of disease state). To achieve this, instead of deriving modes of variation in the directions of maximal variation as in PCA, PLS searches for new axes of variation that correlate most with some output clinical response variables such as diagnostic labels, leading to a decomposition that is anatomically and clinically more meaningful. The validation carried out with 200 cases from the Cardiac Atlas Project database as part of the MICCAI 2015 challenge on SSM, including healthy and infarcted left ventricles, shows the strength of the proposed PLS-based statistical shape model, with 98% prediction accuracy.
UR - http://www.scopus.com/inward/record.url?scp=84955250412&partnerID=8YFLogxK
U2 - 10.1007/978-3-319-28712-6_14
DO - 10.1007/978-3-319-28712-6_14
M3 - Conference contribution
AN - SCOPUS:84955250412
SN - 9783319287119
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 130
EP - 139
BT - Statistical Atlases and Computational Models of the Heart. Imaging and Modelling Challenges
A2 - Camara, Oscar
A2 - Mansi, Tommaso
A2 - Pop, Mihaela
A2 - Rhode, Kawal
A2 - Sermesant, Maxime
A2 - Young, Alistair
PB - Springer Cham
CY - Cham
Y2 - 9 October 2015 through 9 October 2015
ER -