| Title |
Multi-class Segmentation of Anatomical Structures Using Deep Learning in CBCT Images Containing Metal Artifacts |
| Authors |
양수(Su Yang) ; 천소영(Soyoung Chun) ; 김다엘(Dael Kim) ; 전보성(Bo Soung Jeoun) ; 유지용(Jiyong Yoo) ; 강세룡(Se-Ryong Kang) ; 최민혁(Min-Hyuk Choi) ; 김조은(Jo-Eun Kim) ; 허경회(Kyung-Hoe Huh ?) ; 이삼선(Sam-Sun Lee) ; 허민석(Min-Suk Heo) ; 이원진(Won-Jin Yi) |
| DOI |
https://doi.org/10.5370/KIEE.2022.71.1.253 |
| Keywords |
Deep learning; Anatomical structure segmentation; Metal artifacts; U-Net; Tversky loss |
| Abstract |
In order to perform preoperative surgical planning, accurate segmentation of anatomical structures in cone-beam computed tomography (CBCT) images is required. However, this image segmentation is often impeded by metal artifacts, and it takes a lot of time due to morphological variability in patients. In this paper, we proposed a deep learning based automatic multi-calss segmentation method for anatomical structures in CBCT images containing metal artifacts. Four U-Net based deep learning models were used for anatomical structure segmentation. Each deep learning model was constructed by changing the encoder of U-Net architecture to the backbones (DenseNet121, VGGNet16, ResNet101, and EfficienNetB4). For training and testing our method, we used 20744 CBCT images containing metal artifacts from 30 patient datasets. Experimental results show that the segmentation performances of the mandible, midfacial bone, mandibular canal, and maxillary sinus were achieved F1 scores of 0.912±0.070, 0.880±0.080, 0.687±0.265, and 0.954±0.063 using DenseNet121 with Tversky loss, respectively. Furthermore, our method was able to perform robust and accurate segmentation of anatomical structures in CBCT images containing metal artifacts. |