Context-guided fully convolutional networks for joint craniomaxillofacial bone segmentation and landmark digitization

•A joint learning framework for both bone segmentation and landmark digitization.•A displacement map is used to explicitly model the spatial context information.•Results achieved by our method are clinically acceptable.•Only 1 min to complete both tasks of bone segmentation and landmark digitizatio...

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Vydáno v:	Medical image analysis Ročník 60; s. 101621
Hlavní autoři:	Zhang, Jun, Liu, Mingxia, Wang, Li, Chen, Si, Yuan, Peng, Li, Jianfu, Shen, Steve Guo-Fang, Tang, Zhen, Chen, Ken-Chung, Xia, James J., Shen, Dinggang
Médium:	Journal Article
Jazyk:	angličtina
Vydáno:	Netherlands Elsevier B.V 01.02.2020 Elsevier BV
Témata:	Anatomic Landmarks - diagnostic imaging Bone segmentation Computed tomography Cone-beam computed tomography Cone-Beam Computed Tomography - methods Digitization Displacement Fully convolutional networks Humans Image Processing, Computer-Assisted - methods Image segmentation Imaging, Three-Dimensional - methods Joints (anatomy) Landmark digitization Mandible Mapping Maxillofacial Abnormalities - diagnostic imaging Neural Networks, Computer Orthodontics Three dimensional models Bone segmentation Fully convolutional networks Cone-beam computed tomography Landmark digitization
ISSN:	1361-8415, 1361-8423, 1361-8423
On-line přístup:	Získat plný text
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