TY - GEN
T1 - Joint clustering and component analysis of spatio-temporal shape patterns in myocardial infarction
AU - Pinto, Catarina
AU - Çimen, Serkan
AU - Gooya, Ali
AU - Lekadir, Karim
AU - Frangi, Alejandro F.
N1 - Publisher Copyright:
© Springer International Publishing Switzerland 2016.
PY - 2016/1/9
Y1 - 2016/1/9
N2 - The Left Ventricle (LV) undergoes remodelling after Myocardial Infarction (MI). In order to quantify the remodelling status, clinicians make use of conventional measures, not fully exploiting the available shape information. To characterize the changes in heart shape and classify heart data as normal or infarcted, we use a hierarchical generative model, which jointly clusters shape point sets from LV in End-Systolic (ED) and End-Systolic (ES) phases, and estimates the probability density function (pdf) of each cluster. We use a Variational Bayes (VB) method to infer the clusters labels, the mean models, and variation modes for the clusters. We also present the results in the supervised setting, where the labels of training data sets are given. Our classification results are evaluated in terms of sensitivity, specificity, and accuracy using 200 LV shapes provided by MICCAI 2015 STACOM LV Statistical Shape Modelling Challenge. Our method successfully classifies the data, achieving a specificity of 0.92±0.06 and a sensitivity of 0.96±0.07 for the supervised learning approach, and a specificity of 0.83±0.03 and a sensitivity of 0.97±0.01 for the unsupervised learning approach.
AB - The Left Ventricle (LV) undergoes remodelling after Myocardial Infarction (MI). In order to quantify the remodelling status, clinicians make use of conventional measures, not fully exploiting the available shape information. To characterize the changes in heart shape and classify heart data as normal or infarcted, we use a hierarchical generative model, which jointly clusters shape point sets from LV in End-Systolic (ED) and End-Systolic (ES) phases, and estimates the probability density function (pdf) of each cluster. We use a Variational Bayes (VB) method to infer the clusters labels, the mean models, and variation modes for the clusters. We also present the results in the supervised setting, where the labels of training data sets are given. Our classification results are evaluated in terms of sensitivity, specificity, and accuracy using 200 LV shapes provided by MICCAI 2015 STACOM LV Statistical Shape Modelling Challenge. Our method successfully classifies the data, achieving a specificity of 0.92±0.06 and a sensitivity of 0.96±0.07 for the supervised learning approach, and a specificity of 0.83±0.03 and a sensitivity of 0.97±0.01 for the unsupervised learning approach.
UR - http://www.scopus.com/inward/record.url?scp=84955292633&partnerID=8YFLogxK
U2 - 10.1007/978-3-319-28712-6_19
DO - 10.1007/978-3-319-28712-6_19
M3 - Conference contribution
SN - 9783319287119
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 171
EP - 179
BT - Statistical Atlases and Computational Models of the Heart
A2 - Rhode, Kawal
A2 - Camara, Oscar
A2 - Young, Alistair
A2 - Mansi, Tommaso
A2 - Sermesant, Maxime
A2 - Pop, Mihaela
PB - Springer-Verlag Italia
T2 - 6th International Workshop on Statistical Atlases and Computational Models of the Heart, STACOM 2015
Y2 - 9 October 2015 through 9 October 2015
ER -