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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Vydané v:	Medical image analysis Ročník 60; s. 101621
Hlavní autori:	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:	English
Vydavateľské údaje:	Netherlands Elsevier B.V 01.02.2020 Elsevier BV
Predmet:	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
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Abstract	•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 digitization. [Display omitted] Cone-beam computed tomography (CBCT) scans are commonly used in diagnosing and planning surgical or orthodontic treatment to correct craniomaxillofacial (CMF) deformities. Based on CBCT images, it is clinically essential to generate an accurate 3D model of CMF structures (e.g., midface, and mandible) and digitize anatomical landmarks. This process often involves two tasks, i.e., bone segmentation and anatomical landmark digitization. Because landmarks usually lie on the boundaries of segmented bone regions, the tasks of bone segmentation and landmark digitization could be highly associated. Also, the spatial context information (e.g., displacements from voxels to landmarks) in CBCT images is intuitively important for accurately indicating the spatial association between voxels and landmarks. However, most of the existing studies simply treat bone segmentation and landmark digitization as two standalone tasks without considering their inherent relationship, and rarely take advantage of the spatial context information contained in CBCT images. To address these issues, we propose a Joint bone Segmentation and landmark Digitization (JSD) framework via context-guided fully convolutional networks (FCNs). Specifically, we first utilize displacement maps to model the spatial context information in CBCT images, where each element in the displacement map denotes the displacement from a voxel to a particular landmark. An FCN is learned to construct the mapping from the input image to its corresponding displacement maps. Using the learned displacement maps as guidance, we further develop a multi-task FCN model to perform bone segmentation and landmark digitization jointly. We validate the proposed JSD method on 107 subjects, and the experimental results demonstrate that our method is superior to the state-of-the-art approaches in both tasks of bone segmentation and landmark digitization.
AbstractList	Cone-beam computed tomography (CBCT) scans are commonly used in diagnosing and planning surgical or orthodontic treatment to correct craniomaxillofacial (CMF) deformities. Based on CBCT images, it is clinically essential to generate an accurate 3D model of CMF structures (e.g., midface, and mandible) and digitize anatomical landmarks. This process often involves two tasks, i.e., bone segmentation and anatomical landmark digitization. Because landmarks usually lie on the boundaries of segmented bone regions, the tasks of bone segmentation and landmark digitization could be highly associated. Also, the spatial context information (e.g., displacements from voxels to landmarks) in CBCT images is intuitively important for accurately indicating the spatial association between voxels and landmarks. However, most of the existing studies simply treat bone segmentation and landmark digitization as two standalone tasks without considering their inherent relationship, and rarely take advantage of the spatial context information contained in CBCT images. To address these issues, we propose a Joint bone Segmentation and landmark Digitization (JSD) framework via context-guided fully convolutional networks (FCNs). Specifically, we first utilize displacement maps to model the spatial context information in CBCT images, where each element in the displacement map denotes the displacement from a voxel to a particular landmark. An FCN is learned to construct the mapping from the input image to its corresponding displacement maps. Using the learned displacement maps as guidance, we further develop a multi-task FCN model to perform bone segmentation and landmark digitization jointly. We validate the proposed JSD method on 107 subjects, and the experimental results demonstrate that our method is superior to the state-of-the-art approaches in both tasks of 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 digitization. [Display omitted] Cone-beam computed tomography (CBCT) scans are commonly used in diagnosing and planning surgical or orthodontic treatment to correct craniomaxillofacial (CMF) deformities. Based on CBCT images, it is clinically essential to generate an accurate 3D model of CMF structures (e.g., midface, and mandible) and digitize anatomical landmarks. This process often involves two tasks, i.e., bone segmentation and anatomical landmark digitization. Because landmarks usually lie on the boundaries of segmented bone regions, the tasks of bone segmentation and landmark digitization could be highly associated. Also, the spatial context information (e.g., displacements from voxels to landmarks) in CBCT images is intuitively important for accurately indicating the spatial association between voxels and landmarks. However, most of the existing studies simply treat bone segmentation and landmark digitization as two standalone tasks without considering their inherent relationship, and rarely take advantage of the spatial context information contained in CBCT images. To address these issues, we propose a Joint bone Segmentation and landmark Digitization (JSD) framework via context-guided fully convolutional networks (FCNs). Specifically, we first utilize displacement maps to model the spatial context information in CBCT images, where each element in the displacement map denotes the displacement from a voxel to a particular landmark. An FCN is learned to construct the mapping from the input image to its corresponding displacement maps. Using the learned displacement maps as guidance, we further develop a multi-task FCN model to perform bone segmentation and landmark digitization jointly. We validate the proposed JSD method on 107 subjects, and the experimental results demonstrate that our method is superior to the state-of-the-art approaches in both tasks of bone segmentation and landmark digitization. Cone-beam computed tomography (CBCT) scans are commonly used in diagnosing and planning surgical or orthodontic treatment to correct craniomaxillofacial (CMF) deformities. Based on CBCT images, it is clinically essential to generate an accurate 3D model of CMF structures (e.g., midface, and mandible) and digitize anatomical landmarks. This process often involves two tasks, i.e., bone segmentation and anatomical landmark digitization. Because landmarks usually lie on the boundaries of segmented bone regions, the tasks of bone segmentation and landmark digitization could be highly associated. Also, the spatial context information (e.g., displacements from voxels to landmarks) in CBCT images is intuitively important for accurately indicating the spatial association between voxels and landmarks. However, most of the existing studies simply treat bone segmentation and landmark digitization as two standalone tasks without considering their inherent relationship, and rarely take advantage of the spatial context information contained in CBCT images. To address these issues, we propose a Joint bone Segmentation and landmark Digitization (JSD) framework via context-guided fully convolutional networks (FCNs). Specifically, we first utilize displacement maps to model the spatial context information in CBCT images, where each element in the displacement map denotes the displacement from a voxel to a particular landmark. An FCN is learned to construct the mapping from the input image to its corresponding displacement maps. Using the learned displacement maps as guidance, we further develop a multi-task FCN model to perform bone segmentation and landmark digitization jointly. We validate the proposed JSD method on 107 subjects, and the experimental results demonstrate that our method is superior to the state-of-the-art approaches in both tasks of bone segmentation and landmark digitization.Cone-beam computed tomography (CBCT) scans are commonly used in diagnosing and planning surgical or orthodontic treatment to correct craniomaxillofacial (CMF) deformities. Based on CBCT images, it is clinically essential to generate an accurate 3D model of CMF structures (e.g., midface, and mandible) and digitize anatomical landmarks. This process often involves two tasks, i.e., bone segmentation and anatomical landmark digitization. Because landmarks usually lie on the boundaries of segmented bone regions, the tasks of bone segmentation and landmark digitization could be highly associated. Also, the spatial context information (e.g., displacements from voxels to landmarks) in CBCT images is intuitively important for accurately indicating the spatial association between voxels and landmarks. However, most of the existing studies simply treat bone segmentation and landmark digitization as two standalone tasks without considering their inherent relationship, and rarely take advantage of the spatial context information contained in CBCT images. To address these issues, we propose a Joint bone Segmentation and landmark Digitization (JSD) framework via context-guided fully convolutional networks (FCNs). Specifically, we first utilize displacement maps to model the spatial context information in CBCT images, where each element in the displacement map denotes the displacement from a voxel to a particular landmark. An FCN is learned to construct the mapping from the input image to its corresponding displacement maps. Using the learned displacement maps as guidance, we further develop a multi-task FCN model to perform bone segmentation and landmark digitization jointly. We validate the proposed JSD method on 107 subjects, and the experimental results demonstrate that our method is superior to the state-of-the-art approaches in both tasks of bone segmentation and landmark digitization.
ArticleNumber	101621
Author	Liu, Mingxia Wang, Li Shen, Steve Guo-Fang Yuan, Peng Xia, James J. Chen, Ken-Chung Shen, Dinggang Chen, Si Li, Jianfu Tang, Zhen Zhang, Jun
AuthorAffiliation	b Department of Orthodontics, Peking University School and Hospital of Stomatology, Beijing 100191, China a Department of Radiology and BRIC, University of North Carolina, Chapel Hill, NC, 27599, USA d Department of Brain and Cognitive Engineering, Korea University, Seoul 02841, Republic of Korea c Surgical Planning Laboratory, Department of Oral and Maxillofacial Surgery, Houston Methodist Research Institute, Houston, Texas 77030, USA
AuthorAffiliation_xml	– name: c Surgical Planning Laboratory, Department of Oral and Maxillofacial Surgery, Houston Methodist Research Institute, Houston, Texas 77030, USA – name: b Department of Orthodontics, Peking University School and Hospital of Stomatology, Beijing 100191, China – name: d Department of Brain and Cognitive Engineering, Korea University, Seoul 02841, Republic of Korea – name: a Department of Radiology and BRIC, University of North Carolina, Chapel Hill, NC, 27599, USA
Author_xml	– sequence: 1 givenname: Jun surname: Zhang fullname: Zhang, Jun email: xdzhangjun@gmail.com organization: Department of Radiology and BRIC, University of North Carolina, Chapel Hill, NC 27599, USA – sequence: 2 givenname: Mingxia surname: Liu fullname: Liu, Mingxia email: mingxia_liu@med.unc.edu organization: Department of Radiology and BRIC, University of North Carolina, Chapel Hill, NC 27599, USA – sequence: 3 givenname: Li surname: Wang fullname: Wang, Li email: li_wang@med.unc.edu organization: Department of Radiology and BRIC, University of North Carolina, Chapel Hill, NC 27599, USA – sequence: 4 givenname: Si surname: Chen fullname: Chen, Si organization: Department of Orthodontics, Peking University School and Hospital of Stomatology, Beijing 100191, China – sequence: 5 givenname: Peng surname: Yuan fullname: Yuan, Peng organization: Surgical Planning Laboratory, Department of Oral and Maxillofacial Surgery, Houston Methodist Research Institute, Houston, TX 77030, USA – sequence: 6 givenname: Jianfu surname: Li fullname: Li, Jianfu organization: Surgical Planning Laboratory, Department of Oral and Maxillofacial Surgery, Houston Methodist Research Institute, Houston, TX 77030, USA – sequence: 7 givenname: Steve Guo-Fang surname: Shen fullname: Shen, Steve Guo-Fang organization: Surgical Planning Laboratory, Department of Oral and Maxillofacial Surgery, Houston Methodist Research Institute, Houston, TX 77030, USA – sequence: 8 givenname: Zhen surname: Tang fullname: Tang, Zhen organization: Surgical Planning Laboratory, Department of Oral and Maxillofacial Surgery, Houston Methodist Research Institute, Houston, TX 77030, USA – sequence: 9 givenname: Ken-Chung surname: Chen fullname: Chen, Ken-Chung organization: Surgical Planning Laboratory, Department of Oral and Maxillofacial Surgery, Houston Methodist Research Institute, Houston, TX 77030, USA – sequence: 10 givenname: James J. surname: Xia fullname: Xia, James J. email: jxia@houstonmethodist.org organization: Surgical Planning Laboratory, Department of Oral and Maxillofacial Surgery, Houston Methodist Research Institute, Houston, TX 77030, USA – sequence: 11 givenname: Dinggang surname: Shen fullname: Shen, Dinggang email: dinggang_shen@med.unc.edu organization: Department of Radiology and BRIC, University of North Carolina, Chapel Hill, NC 27599, USA
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Snippet	•A joint learning framework for both bone segmentation and landmark digitization.•A displacement map is used to explicitly model the spatial context... Cone-beam computed tomography (CBCT) scans are commonly used in diagnosing and planning surgical or orthodontic treatment to correct craniomaxillofacial (CMF)...
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SubjectTerms	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
Title	Context-guided fully convolutional networks for joint craniomaxillofacial bone segmentation and landmark digitization
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