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최민혁(MinHyuk Choi),강세룡(SeRyong Kang),유지용(JiYong Yoo),양수(Su Yang),김다엘(Da-El Kim),김수정(SuJeong Kim),한지용(JiYong Han),임상헌(SangHeon Lim),송다현(Dahyun Song),김고은(Goeun Kim),용권순(KwonSoon Yong),이원진(WonJin Yi) 대한전자공학회 2023 대한전자공학회 학술대회 Vol.2023 No.6
In dental imaging, Cone-beam computed tomography(CBCT) is widely used due to its high resolution, low radiation dose, and cost. However, motion artifact is a frequent issue due to difficulty in maintaining a stable posture during long scanning time. Therefore, we propose a method of reducing the motion artifact in CBCT imaging using D-Neural Radiance Fields(NeRF) generating a novel synthesized images in dynamic scenes. The experimental results indicate our model significantly outperforms other reconstruction tasks with three motion.
A deep learning based method for maxillofacial bone segmentation in CBCT images
Su Yang(양수),Se-Ryong Kang(강세룡),So-Young Chun(천소영),Ji-Yong Yoo(유지용),Jin Kim(김진),Da El Kim(김다엘),Won-Jin Yi(이원진) 대한전기학회 2021 대한전기학회 학술대회 논문집 Vol.2021 No.10
Cone-beam computed tomography (CBCT) images of the oral and maxillofacial region are commonly used in diagnosing and planning for surgical or orthodontic treatment to correct maxillofacial deformities. It is clinically essential to reconstruct a three dimensional model of maxillofacial structures for orthognathic surgery planning. However, this manual process is very tedious, challenging, and time-consuming. To resolve this problem, we proposed a deep learning based method for mandible and maxilla segmentation in CBCT images. Experimental results show that the proposed network achieves higher performance in all tasks than the baseline segmentation method.