The work proposed in this application aims to continue the maintenance, expansion, validation and dissemination of the SPHARM-PDM (SPherical HARMonics Point Distributed Models) shape analysis toolbox. Shape analysis is of high interest to the biomedical sciences due to its potential to precisely locate morphological changes in pathological structures. SPHARM-PDM is a successful and popular open-source software tool that has been used by the biomedical research community to analyze the morphology of anatomical structures. Most of the existing shape analysis packages lack of integration with publicly available segmentation software tools and are very complicated to use in general. These are the main reasons why shape analysis studies have been performed mainly by larger labs or through close collaboration with computer scientists that specialize in shape analysis. With this proposal we look into maintaining the core functionalities that made of SPHARM-PDM a successful toolbox, as well as into providing a close integration with important segmentation packages and dramatically extending the toolbox into a general shape analysis toolbox (Shape AnaLysis Toolbox, SALT). New methodologies will provide support for objects of non-spherical topology, skeletal models, four-dimensional (4D) models, and study-specific optimal model correspondence. All new methodologies of SALT will be then tested using three application case scenarios to evaluate the efficacy of the proposed new functionalities and to provide an illustration of usability and best practices for the user community. In parallel with th major development and maintenance efforts, we will continue providing excellent documentation, community resources, and user support.
This project will develop a comprehensive software that will allow biomedical scientists to precisely locate shape changes in their imaging studies. This software called Shape AnaLysis Toolbox (SALT), will enhance the intuitiveness and ease of use for such studies, as well as allow researchers to find shape changes with higher statistical power. Altogether this constitutes a crucial resource for the imaging field that will enable many and important new findings in biomedical imaging studies.
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| Mostapha, Mahmoud; Kim, SunHyung; Wu, Guorong et al. (2018) NON-EUCLIDEAN, CONVOLUTIONAL LEARNING ON CORTICAL BRAIN SURFACES. Proc IEEE Int Symp Biomed Imaging 2018:527-530 |
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