박승빈 김한나 심응준 황보연 김영준 이정우 서현석 2024-01-19T12:34:16Z 2024-01-19T12:34:16Z 2022-02-17 2022-02 https://pubs.kist.re.kr/handle/201004/115683 Sophisticated segmentation of the craniomaxillofacial bones (the mandible and maxilla) in computed tomography (CT) is essential for diagnosis and treatment planning for craniomaxillofacial surgeries. Conventional manual segmentation is time-consuming and challenging due to intrinsic properties of craniomaxillofacial bones and head CT such as the variance in the anatomical structures, low contrast of soft tissue, and artifacts caused by metal implants. However, data-driven segmentation methods, including deep learning, require a large consistent dataset, which creates a bottleneck in their clinical applications due to limited datasets. In this study, we propose a deep learning approach for the automatic segmentation of the mandible and maxilla in CT images and enhanced the compatibility for multi-center datasets. Four multi-center datasets acquired by various conditions were applied to create a scenario where the model was trained with one dataset and evaluated with the other datasets. For the neural network, we designed a hierarchical, parallel and multi-scale residual block to the U-Net (HPMR-U-Net). To evaluate the performance, segmentation with in-house dataset and with external datasets from multi-center were conducted in comparison to three other neural networks: U-Net, Res-U-Net and mU-Net. The results suggest that the segmentation performance of HPMR-U-Net is comparable to that of other models, with superior data compatibility. English MDPI Deep Learning-Based Automatic Segmentation of Mandible and Maxilla in Multi-Center CT Images Article 10.3390/app12031358 1 Applied Sciences-basel, v.12, no.3 Applied Sciences-basel 12 3 Y scie scopus 000759968200001 Chemistry, Multidisciplinary Engineering, Multidisciplinary Materials Science, Multidisciplinary Physics, Applied Chemistry Engineering Materials Science Physics AUTO-SEGMENTATION HEAD NETWORKS segmentation mandible craniomaxillofacial bone deep learning neural network multi-center