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430 results on '"Huh, Kyung-Hoe"'

26. Automatic segmentation and classification of frontal sinuses for sex determination from CBCT scans using a two-stage anatomy-guided attention network: A study of the South Korean population

Author

Silva, Renan Lucio Berbel, primary, Yang, Su, additional, Kim, Dael, additional, Kim, Jun Ho, additional, Lim, Sang-Heon, additional, Han, Jiyong, additional, Kim, Jun-Min, additional, Kim, Jo-Eun, additional, Huh, Kyung-Hoe, additional, Lee, Sam-Sun, additional, Heo, Min-Suk, additional, and Yi, Won-Jin, additional

Published
2023
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31. Clinical and radiological features of malformed mesiodens in the nasopalatine canal: an observational study.

Author

Kim, Yu-Ri, Lee, Yu-Min, Huh, Kyung-Hoe, Yi, Won-Jin, Heo, Min-Suk, Lee, Sam-Sun, and Kim, Jo-Eun

Subjects
SUPERNUMERARY teeth, THREE-dimensional imaging, CONE beam computed tomography, COMPUTED tomography, SCIENTIFIC observation, MEDICAL records
Abstract

Objectives The purpose of this study is to investigate the morphological changes that occur when mesiodens is located within the nasopalatine canal, as well as clinical characteristics. Methods Clinical records and CT images of patients who had mesiodens in the nasopalatine canal were retrospectively analysed. In addition to demographic information, clinical symptoms and complications associated with extraction of mesiodens were recorded. Using CT images, number, location, size, and tooth morphology were evaluated. Results This study included 32 patients and 38 mesiodens within the nasopalatine canal. Supernumerary teeth exhibited a characteristic feature of thin and elongated shape in the canal (narrow width and elongation were observed in 96.6% and 53.3% of the patients, respectively). Fusion was found in 4 patients and dilaceration in 12. A complication occurred in 2 patients, which was tooth remnant, not a neurologic complication. Only 5 mesiodens could be detected in the nasopalatine canal on panoramic images. Conclusions Morphological abnormalities in mesiodens within the nasopalatine canal were frequently detected, and these could be effectively diagnosed through 3D imaging analysis. [ABSTRACT FROM AUTHOR]

Published
2024
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32. Automatic detection of posterior superior alveolar artery in dental cone-beam CT images using a deeply supervised multi-scale 3D network.

Author

Park, Jae-An, Kim, DaEl, Yang, Su, Kang, Ju-Hee, Kim, Jo-Eun, Huh, Kyung-Hoe, Lee, Sam-Sun, Yi, Won-Jin, and Heo, Min-Suk

Subjects
CONE beam computed tomography, COMPUTED tomography, DEEP learning, MAXILLOFACIAL surgery, ARTERIES
Abstract

Objectives This study aimed to develop a robust and accurate deep learning network for detecting the posterior superior alveolar artery (PSAA) in dental cone-beam CT (CBCT) images, focusing on the precise localization of the centre pixel as a critical centreline pixel. Methods PSAA locations were manually labelled on dental CBCT data from 150 subjects. The left maxillary sinus images were horizontally flipped. In total, 300 datasets were created. Six different deep learning networks were trained, including 3D U-Net, deeply supervised 3D U-Net (3D U-Net DS), multi-scale deeply supervised 3D U-Net (3D U-Net MSDS), 3D Attention U-Net, 3D V-Net, and 3D Dense U-Net. The performance evaluation involved predicting the centre pixel of the PSAA. This was assessed using mean absolute error (MAE), mean radial error (MRE), and successful detection rate (SDR). Results The 3D U-Net MSDS achieved the best prediction performance among the tested networks, with an MAE measurement of 0.696 ± 1.552 mm and MRE of 1.101 ± 2.270 mm. In comparison, the 3D U-Net showed the lowest performance. The 3D U-Net MSDS demonstrated a SDR of 95% within a 2 mm MAE. This was a significantly higher result than other networks that achieved a detection rate of over 80%. Conclusions This study presents a robust deep learning network for accurate PSAA detection in dental CBCT images, emphasizing precise centre pixel localization. The method achieves high accuracy in locating small vessels, such as the PSAA, and has the potential to enhance detection accuracy and efficiency, thus impacting oral and maxillofacial surgery planning and decision-making. [ABSTRACT FROM AUTHOR]

Published
2024
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