Lang, Y., Wang, L., Yap, P. T., Lian, C., Deng, H., Thung, K. H., Xiao, D., Yuan, P., Shen, S. G. F., Gateno, J., Kuang, T., Alfi, D. M., Xia, J. J., & Shen, D. (2019). Automatic Detection of Craniomaxillofacial Anatomical Landmarks on CBCT Images Using 3D Mask R-CNN. 在 D. Zhang, L. Zhou, B. Jie, & M. Liu (编辑), Graph Learning in Medical Imaging - 1st International Workshop, GLMI 2019, held in Conjunction with MICCAI 2019, Proceedings (页码 130-137). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); 卷 11849 LNCS). Springer. https://doi.org/10.1007/978-3-030-35817-4_16
Lang, Yankun ; Wang, Li ; Yap, Pew Thian 等. / Automatic Detection of Craniomaxillofacial Anatomical Landmarks on CBCT Images Using 3D Mask R-CNN. Graph Learning in Medical Imaging - 1st International Workshop, GLMI 2019, held in Conjunction with MICCAI 2019, Proceedings. 编辑 / Daoqiang Zhang ; Luping Zhou ; Biao Jie ; Mingxia Liu. Springer, 2019. 页码 130-137 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).
@inproceedings{56c759c7d754487f87643f75dc602ade,
title = "Automatic Detection of Craniomaxillofacial Anatomical Landmarks on CBCT Images Using 3D Mask R-CNN",
abstract = "Craniomaxillofacial (CMF) landmark localization is an important step for characterizing jaw deformities and designing surgical plans. However, due to the complexity of facial structure and the deformities of CMF patients, it is still difficult to accurately localize a large scale of landmarks simultaneously. In this work, we propose a three-stage coarse-to-fine deep learning method for digitizing 105 anatomical craniomaxillofacial landmarks on cone-beam computed tomography (CBCT) images. The first stage outputs a coarse location of each landmark from a low-resolution image, which is gradually refined in the next two stages using the corresponding higher resolution images. Our method is implemented using Mask R-CNN, by also incorporating a new loss function that learns the geometrical relationships between the landmarks in the form of a root/leaf structure. We evaluate our approach on 49 CBCT scans of patients and achieve an average detection error of 1.75 ± 0.91 mm. Experimental results show that our approach overperforms the related methods in the term of accuracy.",
author = "Yankun Lang and Li Wang and Yap, {Pew Thian} and Chunfeng Lian and Hannah Deng and Thung, {Kim Han} and Deqiang Xiao and Peng Yuan and Shen, {Steve G.F.} and Jaime Gateno and Tianshu Kuang and Alfi, {David M.} and Xia, {James J.} and Dinggang Shen",
note = "Publisher Copyright: {\textcopyright} 2019, Springer Nature Switzerland AG.; 1st International Workshop on Graph Learning in Medical Imaging, GLMI 2019 held in conjunction with the 22nd International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2019 ; Conference date: 17-10-2019 Through 17-10-2019",
year = "2019",
doi = "10.1007/978-3-030-35817-4_16",
language = "English",
isbn = "9783030358167",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
publisher = "Springer",
pages = "130--137",
editor = "Daoqiang Zhang and Luping Zhou and Biao Jie and Mingxia Liu",
booktitle = "Graph Learning in Medical Imaging - 1st International Workshop, GLMI 2019, held in Conjunction with MICCAI 2019, Proceedings",
address = "Germany",
}
Lang, Y, Wang, L, Yap, PT, Lian, C, Deng, H, Thung, KH, Xiao, D, Yuan, P, Shen, SGF, Gateno, J, Kuang, T, Alfi, DM, Xia, JJ & Shen, D 2019, Automatic Detection of Craniomaxillofacial Anatomical Landmarks on CBCT Images Using 3D Mask R-CNN. 在 D Zhang, L Zhou, B Jie & M Liu (编辑), Graph Learning in Medical Imaging - 1st International Workshop, GLMI 2019, held in Conjunction with MICCAI 2019, Proceedings. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 卷 11849 LNCS, Springer, 页码 130-137, 1st International Workshop on Graph Learning in Medical Imaging, GLMI 2019 held in conjunction with the 22nd International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2019, Shenzhen, 中国, 17/10/19. https://doi.org/10.1007/978-3-030-35817-4_16
Automatic Detection of Craniomaxillofacial Anatomical Landmarks on CBCT Images Using 3D Mask R-CNN. / Lang, Yankun; Wang, Li; Yap, Pew Thian 等.
Graph Learning in Medical Imaging - 1st International Workshop, GLMI 2019, held in Conjunction with MICCAI 2019, Proceedings. 编辑 / Daoqiang Zhang; Luping Zhou; Biao Jie; Mingxia Liu. Springer, 2019. 页码 130-137 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); 卷 11849 LNCS).
科研成果: 书/报告/会议事项章节 › 会议稿件 › 同行评审
TY - GEN
T1 - Automatic Detection of Craniomaxillofacial Anatomical Landmarks on CBCT Images Using 3D Mask R-CNN
AU - Lang, Yankun
AU - Wang, Li
AU - Yap, Pew Thian
AU - Lian, Chunfeng
AU - Deng, Hannah
AU - Thung, Kim Han
AU - Xiao, Deqiang
AU - Yuan, Peng
AU - Shen, Steve G.F.
AU - Gateno, Jaime
AU - Kuang, Tianshu
AU - Alfi, David M.
AU - Xia, James J.
AU - Shen, Dinggang
N1 - Publisher Copyright:
© 2019, Springer Nature Switzerland AG.
PY - 2019
Y1 - 2019
N2 - Craniomaxillofacial (CMF) landmark localization is an important step for characterizing jaw deformities and designing surgical plans. However, due to the complexity of facial structure and the deformities of CMF patients, it is still difficult to accurately localize a large scale of landmarks simultaneously. In this work, we propose a three-stage coarse-to-fine deep learning method for digitizing 105 anatomical craniomaxillofacial landmarks on cone-beam computed tomography (CBCT) images. The first stage outputs a coarse location of each landmark from a low-resolution image, which is gradually refined in the next two stages using the corresponding higher resolution images. Our method is implemented using Mask R-CNN, by also incorporating a new loss function that learns the geometrical relationships between the landmarks in the form of a root/leaf structure. We evaluate our approach on 49 CBCT scans of patients and achieve an average detection error of 1.75 ± 0.91 mm. Experimental results show that our approach overperforms the related methods in the term of accuracy.
AB - Craniomaxillofacial (CMF) landmark localization is an important step for characterizing jaw deformities and designing surgical plans. However, due to the complexity of facial structure and the deformities of CMF patients, it is still difficult to accurately localize a large scale of landmarks simultaneously. In this work, we propose a three-stage coarse-to-fine deep learning method for digitizing 105 anatomical craniomaxillofacial landmarks on cone-beam computed tomography (CBCT) images. The first stage outputs a coarse location of each landmark from a low-resolution image, which is gradually refined in the next two stages using the corresponding higher resolution images. Our method is implemented using Mask R-CNN, by also incorporating a new loss function that learns the geometrical relationships between the landmarks in the form of a root/leaf structure. We evaluate our approach on 49 CBCT scans of patients and achieve an average detection error of 1.75 ± 0.91 mm. Experimental results show that our approach overperforms the related methods in the term of accuracy.
UR - http://www.scopus.com/inward/record.url?scp=85076281234&partnerID=8YFLogxK
U2 - 10.1007/978-3-030-35817-4_16
DO - 10.1007/978-3-030-35817-4_16
M3 - Conference contribution
AN - SCOPUS:85076281234
SN - 9783030358167
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 130
EP - 137
BT - Graph Learning in Medical Imaging - 1st International Workshop, GLMI 2019, held in Conjunction with MICCAI 2019, Proceedings
A2 - Zhang, Daoqiang
A2 - Zhou, Luping
A2 - Jie, Biao
A2 - Liu, Mingxia
PB - Springer
T2 - 1st International Workshop on Graph Learning in Medical Imaging, GLMI 2019 held in conjunction with the 22nd International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2019
Y2 - 17 October 2019 through 17 October 2019
ER -
Lang Y, Wang L, Yap PT, Lian C, Deng H, Thung KH 等. Automatic Detection of Craniomaxillofacial Anatomical Landmarks on CBCT Images Using 3D Mask R-CNN. 在 Zhang D, Zhou L, Jie B, Liu M, 编辑, Graph Learning in Medical Imaging - 1st International Workshop, GLMI 2019, held in Conjunction with MICCAI 2019, Proceedings. Springer. 2019. 页码 130-137. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). doi: 10.1007/978-3-030-35817-4_16
