TY - GEN
T1 - Reusability of statistical shape models for the segmentation of severely abnormal hearts
AU - Albà, Xènia
AU - Lekadir, Karim
AU - Hoogendoorn, Corné
AU - Pereanez, Marco
AU - Swift, Andrew J.
AU - Wild, Jim M.
AU - Frangi, Alejandro F.
N1 - Publisher Copyright:
© Springer International Publishing Switzerland 2015.
PY - 2015
Y1 - 2015
N2 - Statistical shape models have been widely employed in cardiac image segmentation. In practice, however, the construction of the models is faced with several challenges, in particular the need for a sufficiently large training database and a detailed delineation of the training images. Moreover, for pathologies that induce severe shape remodeling such as for pulmonary hypertension (PH), a statistical model is rarely capable of encoding the significant and complex variability of the class. This work presents a new approach for the segmentation of abnormal hearts by reusing statistical shape models built from normal population. To this end, a normalization of the pathological image data is first performed towards the space of the normal shape model, which is then used to guide the segmentation process. Subsequently, the model recovered in the space of normal anatomies is propagated back to the pathological images space. Detailed validation with PH image data shows that the method is both accurate and consistent in its segmentation of highly remodeled hearts.
AB - Statistical shape models have been widely employed in cardiac image segmentation. In practice, however, the construction of the models is faced with several challenges, in particular the need for a sufficiently large training database and a detailed delineation of the training images. Moreover, for pathologies that induce severe shape remodeling such as for pulmonary hypertension (PH), a statistical model is rarely capable of encoding the significant and complex variability of the class. This work presents a new approach for the segmentation of abnormal hearts by reusing statistical shape models built from normal population. To this end, a normalization of the pathological image data is first performed towards the space of the normal shape model, which is then used to guide the segmentation process. Subsequently, the model recovered in the space of normal anatomies is propagated back to the pathological images space. Detailed validation with PH image data shows that the method is both accurate and consistent in its segmentation of highly remodeled hearts.
UR - http://www.scopus.com/inward/record.url?scp=84927784745&partnerID=8YFLogxK
U2 - 10.1007/978-3-319-14678-2_27
DO - 10.1007/978-3-319-14678-2_27
M3 - Conference contribution
AN - SCOPUS:84927784745
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 257
EP - 264
BT - Statistical Atlases and Computational Models of the Heart
A2 - Pop, Mihaela
A2 - Mansi, Tommaso
A2 - Camara, Oscar
A2 - Sermesant, Maxime
A2 - Young, Alistair
A2 - Rhode, Kawal
A2 - Rhode, Kawal
PB - Springer-Verlag Italia
T2 - 5th International Workshop on Statistical Atlases and Computational Models of the Heart, STACOM 2014 Held in Conjunction with Medical Image Computing and Computer Assisted Intervention Conference, MICCAI 2014
Y2 - 18 September 2014 through 18 September 2014
ER -
