Mandibular Canal Segmentation From CBCT Image Using 3D Convolutional Neural Network With scSE Attention
In dental implant planning, the mandibular canal is an important reference for determining the safe position of the implant. Accurate and automatic segmentation of the mandibular canal from CBCT image is of great significance. However, the variable curvature of the mandibular canal and the blurred b...
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| Format: | Article |
| Language: | English |
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IEEE
2022-01-01
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| Series: | IEEE Access |
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| Online Access: | https://ieeexplore.ieee.org/document/9916261/ |
| _version_ | 1811257375516000256 |
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| author | Gang Du Xinyu Tian Yixu Song |
| author_facet | Gang Du Xinyu Tian Yixu Song |
| author_sort | Gang Du |
| collection | DOAJ |
| description | In dental implant planning, the mandibular canal is an important reference for determining the safe position of the implant. Accurate and automatic segmentation of the mandibular canal from CBCT image is of great significance. However, the variable curvature of the mandibular canal and the blurred borders make the process challenging. At present, the segmentation of mandibular canal is usually carried out by experienced, doctors using manual or semi-automatic methods, which are time-consuming and have poor segmentation consistency. For these issues, this paper proposes a mandibular canal segmentation method based on 3D convolutional neural network. Firstly, a mandibular canal segmentation method based on center line combined with region growth and a mandibular canal clipping method based on fixed points are proposed to improve the efficiency of mandibular canal marking and reduce the imbalance between sample categories, respectively. Thereafter, the scSE attention module was added to the network for reducing irrelevant background information. Finally, an weighted BCE loss function was used to prevent the mental foramen and mandibular foramen areas from participating in the back-propagation calculation, and the network is more focused on the feature learning of the mandibular canal. The experimental results show that the proposed segmentation method achieves good segmentation results, with a Dice score of 85.9% and a 95% Hausdorff distance of 0.5371mm. Compared with other methods, the segmentation method proposed in this paper had a higher accuracy and a faster efficiency, which is expected to play a practical role in dental implants. |
| first_indexed | 2024-04-12T17:55:28Z |
| format | Article |
| id | doaj.art-42859a1a990348ccb2780e15f814b8b9 |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-04-12T17:55:28Z |
| publishDate | 2022-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-42859a1a990348ccb2780e15f814b8b92022-12-22T03:22:21ZengIEEEIEEE Access2169-35362022-01-011011127211128310.1109/ACCESS.2022.32138399916261Mandibular Canal Segmentation From CBCT Image Using 3D Convolutional Neural Network With scSE AttentionGang Du0Xinyu Tian1https://orcid.org/0000-0003-3673-2628Yixu Song2https://orcid.org/0000-0002-3477-4886School of Information Engineering, China University of Geosciences, Beijing, ChinaSchool of Information Engineering, China University of Geosciences, Beijing, ChinaDepartment of Computer Science and Technology, Tsinghua University, Beijing, ChinaIn dental implant planning, the mandibular canal is an important reference for determining the safe position of the implant. Accurate and automatic segmentation of the mandibular canal from CBCT image is of great significance. However, the variable curvature of the mandibular canal and the blurred borders make the process challenging. At present, the segmentation of mandibular canal is usually carried out by experienced, doctors using manual or semi-automatic methods, which are time-consuming and have poor segmentation consistency. For these issues, this paper proposes a mandibular canal segmentation method based on 3D convolutional neural network. Firstly, a mandibular canal segmentation method based on center line combined with region growth and a mandibular canal clipping method based on fixed points are proposed to improve the efficiency of mandibular canal marking and reduce the imbalance between sample categories, respectively. Thereafter, the scSE attention module was added to the network for reducing irrelevant background information. Finally, an weighted BCE loss function was used to prevent the mental foramen and mandibular foramen areas from participating in the back-propagation calculation, and the network is more focused on the feature learning of the mandibular canal. The experimental results show that the proposed segmentation method achieves good segmentation results, with a Dice score of 85.9% and a 95% Hausdorff distance of 0.5371mm. Compared with other methods, the segmentation method proposed in this paper had a higher accuracy and a faster efficiency, which is expected to play a practical role in dental implants.https://ieeexplore.ieee.org/document/9916261/Deep learningimage processingintelligent segmentationoral implantation |
| spellingShingle | Gang Du Xinyu Tian Yixu Song Mandibular Canal Segmentation From CBCT Image Using 3D Convolutional Neural Network With scSE Attention IEEE Access Deep learning image processing intelligent segmentation oral implantation |
| title | Mandibular Canal Segmentation From CBCT Image Using 3D Convolutional Neural Network With scSE Attention |
| title_full | Mandibular Canal Segmentation From CBCT Image Using 3D Convolutional Neural Network With scSE Attention |
| title_fullStr | Mandibular Canal Segmentation From CBCT Image Using 3D Convolutional Neural Network With scSE Attention |
| title_full_unstemmed | Mandibular Canal Segmentation From CBCT Image Using 3D Convolutional Neural Network With scSE Attention |
| title_short | Mandibular Canal Segmentation From CBCT Image Using 3D Convolutional Neural Network With scSE Attention |
| title_sort | mandibular canal segmentation from cbct image using 3d convolutional neural network with scse attention |
| topic | Deep learning image processing intelligent segmentation oral implantation |
| url | https://ieeexplore.ieee.org/document/9916261/ |
| work_keys_str_mv | AT gangdu mandibularcanalsegmentationfromcbctimageusing3dconvolutionalneuralnetworkwithscseattention AT xinyutian mandibularcanalsegmentationfromcbctimageusing3dconvolutionalneuralnetworkwithscseattention AT yixusong mandibularcanalsegmentationfromcbctimageusing3dconvolutionalneuralnetworkwithscseattention |