Abstract
The biggest challenge to improve the diagnosis and therapies of Craniomaxillofacial conditions is to translate algorithms and software developments towards the creation of holistic patient models. A complete picture of the individual patient for treatment planning and personalized healthcare requires a compilation of clinician-friendly algorithms to provide minimally invasive diagnostic techniques with multimodal image integration and analysis. We describe here the implementation of the open-source Craniomaxillofacial module of the 3D Slicer software, as well as its clinical applications. This paper proposes data management approaches for multisource data extraction, registration, visualization, and quantification. These applications integrate medical images with clinical and biological data analytics, user studies, and other heterogeneous data.
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Acknowledgements
This study was supported by NIH grants DE R01DE024450, R21DE025306 and R01 EB021391.
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Bianchi, J. et al. (2020). 3D Slicer Craniomaxillofacial Modules Support Patient-Specific Decision-Making for Personalized Healthcare in Dental Research. In: Syeda-Mahmood, T., et al. Multimodal Learning for Clinical Decision Support and Clinical Image-Based Procedures. CLIP ML-CDS 2020 2020. Lecture Notes in Computer Science(), vol 12445. Springer, Cham. https://doi.org/10.1007/978-3-030-60946-7_5
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DOI: https://doi.org/10.1007/978-3-030-60946-7_5
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