Statistical Shape Modelling of the Nasosinusal Anatomy
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arXiv
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| Autores principales: | , , , , , , , |
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| Formato: | Preprint |
| Publicado: |
2025
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| _version_ | 1866909836774473728 |
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| author | Bertolini, Michele Tonghini, Silvia Rossoni, Marco Carulli, Marina Weise, Susanne Baldus, Christian Jan Colombo, Giorgio Bordegoni, Monica |
| author_facet | Bertolini, Michele Tonghini, Silvia Rossoni, Marco Carulli, Marina Weise, Susanne Baldus, Christian Jan Colombo, Giorgio Bordegoni, Monica |
| contents | The human nose exhibits a huge variation in shape among individuals. All these variants alter the airflow through the nasal cavity and can impact how we smell odors. To acquire a better understanding of physiological and pathological functioning, it is important to study the effects of these modifications. SSM, or Statistical Shape Modelling, is a widely used methodology for considering morphological differences within a population. In the literature, only a few studies analyze nasal anatomy in its entirety, including paranasal sinuses, whose segmentation is particularly challenging due to their complexity and variability. This work aims at creating a highly accurate SSM for the nasosinusal complex. A traditional, greyscale thresholding-based segmentation approach was chosen, relying on Mimics software (Leuven, Belgium). 40 computed tomography (CT) datasets were considered. After segmentation, post-processing was performed to obtain watertight meshes using 3-Matic software (Leuven, Belgium), which was also used for the SSM generation. The generated model shows all relevant landmarks that typically characterize a nasosinual complex. 32 different modes are needed to explain 95% of the total variance. As a preliminary output, this study showed the feasibility and consistency of a statistical model of the nasosinusal anatomy. However, additional studies involving a larger sample size and a more robust validation process could be conducted. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2510_09713 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Statistical Shape Modelling of the Nasosinusal Anatomy Bertolini, Michele Tonghini, Silvia Rossoni, Marco Carulli, Marina Weise, Susanne Baldus, Christian Jan Colombo, Giorgio Bordegoni, Monica Medical Physics Classical Physics The human nose exhibits a huge variation in shape among individuals. All these variants alter the airflow through the nasal cavity and can impact how we smell odors. To acquire a better understanding of physiological and pathological functioning, it is important to study the effects of these modifications. SSM, or Statistical Shape Modelling, is a widely used methodology for considering morphological differences within a population. In the literature, only a few studies analyze nasal anatomy in its entirety, including paranasal sinuses, whose segmentation is particularly challenging due to their complexity and variability. This work aims at creating a highly accurate SSM for the nasosinusal complex. A traditional, greyscale thresholding-based segmentation approach was chosen, relying on Mimics software (Leuven, Belgium). 40 computed tomography (CT) datasets were considered. After segmentation, post-processing was performed to obtain watertight meshes using 3-Matic software (Leuven, Belgium), which was also used for the SSM generation. The generated model shows all relevant landmarks that typically characterize a nasosinual complex. 32 different modes are needed to explain 95% of the total variance. As a preliminary output, this study showed the feasibility and consistency of a statistical model of the nasosinusal anatomy. However, additional studies involving a larger sample size and a more robust validation process could be conducted. |
| title | Statistical Shape Modelling of the Nasosinusal Anatomy |
| topic | Medical Physics Classical Physics |
| url | https://arxiv.org/abs/2510.09713 |