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Automatic segmentation of the structures in the nasal cavity and the ethmoidal sinus for the quantification of nasal septal deviations

Oyarzun Laura, Cristina ; Hartwig, Katrin ; Distergoft, Alexander ; Hoffmann, Tim ; Scheckenbach, Kathrin ; Brüsseler, Melanie ; Wesarg, Stefan ; Drukker, Karen ; Mazurowski, Maciej A. (2021):
Automatic segmentation of the structures in the nasal cavity and the ethmoidal sinus for the quantification of nasal septal deviations.
In: Proceedings SPIE 11597 : Medical Imaging 2021 : Computer-Aided Diagnosis,
SPIE, Medical Imaging Conference 2021, virtual Conference, 15.-19.02.2021, DOI: 10.1117/12.2582251,
[Conference or Workshop Item]

Abstract

Nasal septal deviations are a well-known and widespread problem. According to the American Academy of Otolaryngology 80% of the population have a nasal septal deviation. Its level of severity can range from the person not being aware of it to respiratory obstruction and choking. It is therefore necessary to distinguish those patients at risk. For a proper diagnosis, the amount and location of the deviation have to be considered, but also the shape and changes in the surrounding turbinates. The segmentation of the structures of interest is an important step to reduce subjectivity in the diagnosis. Unfortunately, due to their variable and tortuous shape manual segmentation is time consuming. In this paper, the _rst method for the automatic segmentation of the structures in the nasal cavity and ethmoidal sinus is presented. A coupled shape model of the nasal cavity and paranasal sinus regions is trained and used to detect the corresponding regions in new CT images. The nasal septum is then segmented using a novel slice-based propagation technique. This segmentation allows the additional separation and segmentation of the left and right nasal cavities and ethmoidal sinuses and their structures by means of an adaptive thresholding with varying boundary sizes. The method has been evaluated in 10 CT images obtaining promising results for the nasal septum (DICE: 87.71%) and for the remaining structures (DICE: 72.01% - 73.01%). Based on the resulting segmentations, a web-based diagnosis tool has been designed to quantify the septal deviation using three metrics proposed by clinical experts.

Item Type: Conference or Workshop Item
Erschienen: 2021
Creators: Oyarzun Laura, Cristina ; Hartwig, Katrin ; Distergoft, Alexander ; Hoffmann, Tim ; Scheckenbach, Kathrin ; Brüsseler, Melanie ; Wesarg, Stefan ; Drukker, Karen ; Mazurowski, Maciej A.
Title: Automatic segmentation of the structures in the nasal cavity and the ethmoidal sinus for the quantification of nasal septal deviations
Language: English
Abstract:

Nasal septal deviations are a well-known and widespread problem. According to the American Academy of Otolaryngology 80% of the population have a nasal septal deviation. Its level of severity can range from the person not being aware of it to respiratory obstruction and choking. It is therefore necessary to distinguish those patients at risk. For a proper diagnosis, the amount and location of the deviation have to be considered, but also the shape and changes in the surrounding turbinates. The segmentation of the structures of interest is an important step to reduce subjectivity in the diagnosis. Unfortunately, due to their variable and tortuous shape manual segmentation is time consuming. In this paper, the _rst method for the automatic segmentation of the structures in the nasal cavity and ethmoidal sinus is presented. A coupled shape model of the nasal cavity and paranasal sinus regions is trained and used to detect the corresponding regions in new CT images. The nasal septum is then segmented using a novel slice-based propagation technique. This segmentation allows the additional separation and segmentation of the left and right nasal cavities and ethmoidal sinuses and their structures by means of an adaptive thresholding with varying boundary sizes. The method has been evaluated in 10 CT images obtaining promising results for the nasal septum (DICE: 87.71%) and for the remaining structures (DICE: 72.01% - 73.01%). Based on the resulting segmentations, a web-based diagnosis tool has been designed to quantify the septal deviation using three metrics proposed by clinical experts.

Title of Book: Proceedings SPIE 11597 : Medical Imaging 2021 : Computer-Aided Diagnosis
Publisher: SPIE
Uncontrolled Keywords: Medical imaging, Web applications, Segmentation, Medical diagnosis, Computer aided diagnosis
Divisions: 20 Department of Computer Science
20 Department of Computer Science > Interactive Graphics Systems
Event Title: Medical Imaging Conference 2021
Event Location: virtual Conference
Event Dates: 15.-19.02.2021
Date Deposited: 16 Mar 2021 08:12
DOI: 10.1117/12.2582251
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