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Automatic Teeth Segmentation in Cephalometric X-Ray Images Using a Coupled Shape Model

Wirtz, Andreas and Wambach, Johannes and Wesarg, Stefan (2018):
Automatic Teeth Segmentation in Cephalometric X-Ray Images Using a Coupled Shape Model.
In: OR 2.0 Context-Aware Operating Theaters, Computer Assisted Robotic Endoscopy, Clinical Image-Based Procedures, and Skin Image Analysis, Cham, Springer, In: International Workshop on OR 2.0 Context-Aware Operating Theaters (OR 2.0), Granada, Spain, In: Lecture Notes in Computer Science (LNCS), 11041, ISSN 0302-9743,
DOI: 10.1007/978-3-030-01201-4_21,
[Online-Edition: https://doi.org/10.1007/978-3-030-01201-4_21],
[Conference or Workshop Item]

Abstract

Cephalometric analysis is an important tool used by dentists for diagnosis and treatment of patients. Tools that could automate this time consuming task would be of great assistance. In order to provide the dentist with such tools, a robust and accurate identification of the necessary landmarks is required. However, poor image quality of lateral cephalograms like low contrast or noise as well as duplicate structures resulting from the way these images are acquired make this task difficult. In this paper, a fully automatic approach for teeth segmentation is presented that aims to support the identification of dental landmarks. A 2-D coupled shape model is used to capture the statistical knowledge about the teeth’s shape variation and spatial relation to enable a robust segmentation despite poor image quality. 14 individual teeth are segmented and labeled using gradient image features and the quality of the generated results is compared to manually created gold-standard segmentations. Experimental results on a set of 14 test images show promising results with a DICE overlap of 77.2% and precision and recall values of 82.3% and 75.4%, respectively.

Item Type: Conference or Workshop Item
Erschienen: 2018
Creators: Wirtz, Andreas and Wambach, Johannes and Wesarg, Stefan
Title: Automatic Teeth Segmentation in Cephalometric X-Ray Images Using a Coupled Shape Model
Language: English
Abstract:

Cephalometric analysis is an important tool used by dentists for diagnosis and treatment of patients. Tools that could automate this time consuming task would be of great assistance. In order to provide the dentist with such tools, a robust and accurate identification of the necessary landmarks is required. However, poor image quality of lateral cephalograms like low contrast or noise as well as duplicate structures resulting from the way these images are acquired make this task difficult. In this paper, a fully automatic approach for teeth segmentation is presented that aims to support the identification of dental landmarks. A 2-D coupled shape model is used to capture the statistical knowledge about the teeth’s shape variation and spatial relation to enable a robust segmentation despite poor image quality. 14 individual teeth are segmented and labeled using gradient image features and the quality of the generated results is compared to manually created gold-standard segmentations. Experimental results on a set of 14 test images show promising results with a DICE overlap of 77.2% and precision and recall values of 82.3% and 75.4%, respectively.

Title of Book: OR 2.0 Context-Aware Operating Theaters, Computer Assisted Robotic Endoscopy, Clinical Image-Based Procedures, and Skin Image Analysis
Series Name: Lecture Notes in Computer Science (LNCS)
Volume: 11041
Place of Publication: Cham
Publisher: Springer
Uncontrolled Keywords: Dental imaging, Statistical shape models (SSM), Model based segmentations, Automatic segmentation
Divisions: 20 Department of Computer Science
20 Department of Computer Science > Interactive Graphics Systems
Event Title: International Workshop on OR 2.0 Context-Aware Operating Theaters (OR 2.0)
Event Location: Granada, Spain
Date Deposited: 19 Jun 2019 11:19
DOI: 10.1007/978-3-030-01201-4_21
Official URL: https://doi.org/10.1007/978-3-030-01201-4_21
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