Abstract
In this work, the application of statistical shape analysis to oropharyngeal structures from the population-based MRI data is investigated. For this purpose, statistical shape models (SSMs) of the relevant anatomical structures are created in order to determine the unknown parameters, which influence the shape of these areas. Subsequently, it is determined whether there is a connection between their shape and the occurrence of obstructive sleep apnea syndrome. Two statistical shape modeling approaches are investigated, namely, the classical SSMs constructed from the segmentation masks as well as (TL-)DeepSSM, which allows for extracting the shape models directly from the MRI scans without the segmentation process. The suitability of the methods for our particular application as well as their pros and cons are discussed. Additionally, the shape differences for healthy and diseased subjects using SSMs are presented.
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Acknowledgements
SHIP is part of the Community Medicine Research net of the University of Greifswald, Germany, which is funded by the Federal Ministry of Education and Research (grants no. 01ZZ9603, 01ZZ0103, and 01ZZ0403), the Ministry of Cultural Affairs as well as the Social Ministry of the Federal State of Mecklenburg-West Pomerania, and the network ’Greifswald Approach to Individualized Medicine (GANI_MED)’ funded by the Federal Ministry of Education and Research (grant 03IS2061A). Whole-body MR imaging was supported by a joint grant from Siemens Healthineers, Erlangen, Germany and the Federal State of Mecklenburg West Pomerania. The authors are thankful for the Radiology and the Data Transfer departments at the University Medicine Greifswald for providing the data.
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Schlosser, M., Krüger, M., Daboul, A., Ivanovska, T. (2025). Application of Deep Statistical Shape Modeling for Analysis of Obstructive Sleep Apnea from MRI Data. In: Wachinger, C., Paniagua, B., Elhabian, S., Luijten, G., Egger, J. (eds) Shape in Medical Imaging. ShapeMI 2024. Lecture Notes in Computer Science, vol 15275. Springer, Cham. https://doi.org/10.1007/978-3-031-75291-9_10
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