Statistical Shape Models of the Heart: Applications to Cardiac Imaging

Concetta Piazzese, M. Chiara Carminati, Mauro Pepi, Enrico G. Caiani

Research output: Chapter in Book/Report/Conference proceedingChapterpeer-review

12 Citations (Scopus)


Recent advances in imaging technology have enabled the non-invasive study of the structure and the function of the heart, the valves and the vascular system. Different imaging modalities are routinely used to provide specific and complementary diagnostic and prognostic information.Computerized analysis plays a crucial role to in quantifying cardiac function from non-invasive imaging. To this respect, model-based techniques, such as statistical shape models (SSMs), have become a popular solution for the detection of different cardiac structures. In this two-steps approach, a statistical model, trained on a set of samples to encode the morphology and the statistical variability of the structure of interest, is applied to segment the same structure in new images constraining the possible deformations only to plausible shapes observed in the training set.The aim of this chapter is to give a summary of the current state-of-the-art of SSMs in cardiac imaging. In particular, the most relevant and recent SSMs applications proposed for a specific structure (left ventricle, right ventricle, atria and valves) or more cardiac structures together (left and right ventricles, four chambers and entire heart) will be discussed. Furthermore, the potential usefulness of this technique as well as its robustness when applied to different imaging modalities are reviewed.

Original languageEnglish
Title of host publicationStatistical Shape and Deformation Analysis
Subtitle of host publicationMethods, Implementation and Applications
EditorsGuoyan Zheng, Shuo Li, Gabor Szekely
PublisherElsevier Inc.
Number of pages36
ISBN (Electronic)9780128104941
ISBN (Print)9780128104934
Publication statusPublished - 23 Mar 2017
Externally publishedYes


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