Multi-region statistical shape model for cochlear implantation

Jordi Romera; H. Martin Kjer; Gemma Piella; Mario Ceresa; Miguel A. González Ballester

doi:10.1117/12.2213305

21 March 2016 Multi-region statistical shape model for cochlear implantation

Jordi Romera , H. Martin Kjer, Gemma Piella, Mario Ceresa, Miguel A. González Ballester

Proceedings Volume 9784, Medical Imaging 2016: Image Processing; 97840T (2016) https://doi.org/10.1117/12.2213305
Event: SPIE Medical Imaging, 2016, San Diego, California, United States

Abstract

Statistical shape models are commonly used to analyze the variability between similar anatomical structures and their use is established as a tool for analysis and segmentation of medical images. However, using a global model to capture the variability of complex structures is not enough to achieve the best results. The complexity of a proper global model increases even more when the amount of data available is limited to a small number of datasets. Typically, the anatomical variability between structures is associated to the variability of their physiological regions. In this paper, a complete pipeline is proposed for building a multi-region statistical shape model to study the entire variability from locally identified physiological regions of the inner ear. The proposed model, which is based on an extension of the Point Distribution Model (PDM), is built for a training set of 17 high-resolution images (24.5 μm voxels) of the inner ear. The model is evaluated according to its generalization ability and specificity. The results are compared with the ones of a global model built directly using the standard PDM approach. The evaluation results suggest that better accuracy can be achieved using a regional modeling of the inner ear.

Citation Download Citation

Jordi Romera , H. Martin Kjer, Gemma Piella, Mario Ceresa, and Miguel A. González Ballester "Multi-region statistical shape model for cochlear implantation", Proc. SPIE 9784, Medical Imaging 2016: Image Processing, 97840T (21 March 2016); https://doi.org/10.1117/12.2213305

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