U-net based metal segmentation on projection domain for metal artifact reduction in dental CT
10.1007/s13534-019-00110-2
- Author:
Mohamed A A HEGAZY
1
;
Myung Hye CHO
;
Min Hyoung CHO
;
Soo Yeol LEE
Author Information
1. Department of Biomedical Engineering, Kyung Hee University, Yongin, Korea. sylee01@khu.ac.kr
- Publication Type:Original Article
- Keywords:
Metal artifact reduction;
Metal segmentation;
Deep learning;
U-net;
Dental CT
- MeSH:
Artifacts;
Dental Enamel;
Dental Prosthesis;
Humans;
Methods;
NAD;
Silver Sulfadiazine;
Tooth
- From:
Biomedical Engineering Letters
2019;9(3):375-385
- CountryRepublic of Korea
- Language:English
-
Abstract:
Unlike medical computed tomography (CT), dental CT often suffers from severe metal artifacts stemming from high-density materials employed for dental prostheses. Despite the many metal artifact reduction (MAR) methods available for medical CT, those methods do not sufficiently reduce metal artifacts in dental CT images because MAR performance is often compromised by the enamel layer of teeth, whose X-ray attenuation coefficient is not so different from that of prosthetic materials. We propose a deep learning-based metal segmentation method on the projection domain to improve MAR performance in dental CT. We adopted a simplified U-net for metal segmentation on the projection domain without using any information from the metal-artifacts-corrupted CT images. After training the network with the projection data of five patients, we segmented the metal objects on the projection data of other patients using the trained network parameters. With the segmentation results, we corrected the projection data by applying region filling inside the segmented region. We fused two CT images, one from the corrected projection data and the other from the original raw projection data, and then we forward-projected the fused CT image to get the fused projection data. To get the final corrected projection data, we replaced the metal regions in the original projection data with the ones in the fused projection data. To evaluate the efficacy of the proposed segmentation method on MAR, we compared the MAR performance of the proposed segmentation method with a conventional MAR method based on metal segmentation on the CT image domain. For the MAR performance evaluation, we considered the three primary MAR performance metrics: the relative error (REL), the sum of square difference (SSD), and the normalized absolute difference (NAD). The proposed segmentation method improved MAR performances by around 5.7% for REL, 6.8% for SSD, and 8.2% for NAD. The proposed metal segmentation method on the projection domain showed better MAR performance than the conventional segmentation on the CT image domain. We expect that the proposed segmentation method can improve the performance of the existing MAR methods that are based on metal segmentation on the CT image domain.
