Comparison of 2D, 2.5D, and 3D segmentation networks for mandibular canals in CBCT images: a study on public and external datasets
Abstract The purpose of this study was to compare the performances of 2D, 2.5D, and 3D CNN-based segmentation networks, along with a 3D vision transformer-based segmentation network, for segmenting mandibular canals (MCs) on the public and external CBCT datasets under the same GPU memory capacity. W...
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BMC
2025-07-01
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| Series: | BMC Oral Health |
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| Online Access: | https://doi.org/10.1186/s12903-025-06483-4 |
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| author | Su Yang Jong Soo Jeong Dahyun Song Ji Yong Han Sang-Heon Lim Sujeong Kim Ji-Yong Yoo Jun-Min Kim Jo-Eun Kim Kyung-Hoe Huh Sam-Sun Lee Min-Suk Heo Won-Jin Yi |
| author_facet | Su Yang Jong Soo Jeong Dahyun Song Ji Yong Han Sang-Heon Lim Sujeong Kim Ji-Yong Yoo Jun-Min Kim Jo-Eun Kim Kyung-Hoe Huh Sam-Sun Lee Min-Suk Heo Won-Jin Yi |
| author_sort | Su Yang |
| collection | DOAJ |
| description | Abstract The purpose of this study was to compare the performances of 2D, 2.5D, and 3D CNN-based segmentation networks, along with a 3D vision transformer-based segmentation network, for segmenting mandibular canals (MCs) on the public and external CBCT datasets under the same GPU memory capacity. We also performed ablation studies for an image-cropping (IC) technique and segmentation loss functions. 3D-UNet showed the highest segmentation performance for the MC than those of 2D and 2.5D segmentation networks on public test datasets, achieving 0.569 ± 0.107, 0.719 ± 0.092, 0.664 ± 0.131, and 0.812 ± 0.095 in terms of JI, DSC, PR, and RC, respectively. On the external test dataset, 3D-UNet achieved 0.564 ± 0.092, 0.716 ± 0.081, 0.812 ± 0.087, and 0.652 ± 0.103 in terms of JI, DSC, PR, and RC, respectively. The IC technique and multi-planar Dice loss improved the boundary details and structural connectivity of the MC from the mental foramen to the mandibular foramen. The 3D-UNet demonstrated superior segmentation performance for the MC by learning 3D volumetric context information for the entire MC in the CBCT volume. |
| format | Article |
| id | doaj-art-4cb8f62f4f4e43af8d351c868da382fe |
| institution | DOAJ |
| issn | 1472-6831 |
| language | English |
| publishDate | 2025-07-01 |
| publisher | BMC |
| record_format | Article |
| series | BMC Oral Health |
| spelling | doaj-art-4cb8f62f4f4e43af8d351c868da382fe2025-08-20T03:06:31ZengBMCBMC Oral Health1472-68312025-07-0125111710.1186/s12903-025-06483-4Comparison of 2D, 2.5D, and 3D segmentation networks for mandibular canals in CBCT images: a study on public and external datasetsSu Yang0Jong Soo Jeong1Dahyun Song2Ji Yong Han3Sang-Heon Lim4Sujeong Kim5Ji-Yong Yoo6Jun-Min Kim7Jo-Eun Kim8Kyung-Hoe Huh9Sam-Sun Lee10Min-Suk Heo11Won-Jin Yi12Department of Applied Bioengineering, Graduate School of Convergence Science and Technology, Seoul National UniversityDepartment of Dentistry, School of Dentistry, Seoul National UniversityInterdisciplinary Program in Bioengineering, Graduate School of Engineering, Seoul National UniversityInterdisciplinary Program in Bioengineering, Graduate School of Engineering, Seoul National UniversityInterdisciplinary Program in Bioengineering, Graduate School of Engineering, Seoul National UniversityInterdisciplinary Program in Bioengineering, Graduate School of Engineering, Seoul National UniversityDepartment of Applied Bioengineering, Graduate School of Convergence Science and Technology, Seoul National UniversityDepartment of Electronics and Information Engineering, Hansung UniversityDepartment of Oral and Maxillofacial Radiology and Dental Research Institute, School of Dentistry, Seoul National UniversityDepartment of Oral and Maxillofacial Radiology and Dental Research Institute, School of Dentistry, Seoul National UniversityDepartment of Oral and Maxillofacial Radiology and Dental Research Institute, School of Dentistry, Seoul National UniversityDepartment of Oral and Maxillofacial Radiology and Dental Research Institute, School of Dentistry, Seoul National UniversityDepartment of Applied Bioengineering, Graduate School of Convergence Science and Technology, Seoul National UniversityAbstract The purpose of this study was to compare the performances of 2D, 2.5D, and 3D CNN-based segmentation networks, along with a 3D vision transformer-based segmentation network, for segmenting mandibular canals (MCs) on the public and external CBCT datasets under the same GPU memory capacity. We also performed ablation studies for an image-cropping (IC) technique and segmentation loss functions. 3D-UNet showed the highest segmentation performance for the MC than those of 2D and 2.5D segmentation networks on public test datasets, achieving 0.569 ± 0.107, 0.719 ± 0.092, 0.664 ± 0.131, and 0.812 ± 0.095 in terms of JI, DSC, PR, and RC, respectively. On the external test dataset, 3D-UNet achieved 0.564 ± 0.092, 0.716 ± 0.081, 0.812 ± 0.087, and 0.652 ± 0.103 in terms of JI, DSC, PR, and RC, respectively. The IC technique and multi-planar Dice loss improved the boundary details and structural connectivity of the MC from the mental foramen to the mandibular foramen. The 3D-UNet demonstrated superior segmentation performance for the MC by learning 3D volumetric context information for the entire MC in the CBCT volume.https://doi.org/10.1186/s12903-025-06483-4Deep learningCBCT imageMandibular canalImage segmentation3D segmentation network |
| spellingShingle | Su Yang Jong Soo Jeong Dahyun Song Ji Yong Han Sang-Heon Lim Sujeong Kim Ji-Yong Yoo Jun-Min Kim Jo-Eun Kim Kyung-Hoe Huh Sam-Sun Lee Min-Suk Heo Won-Jin Yi Comparison of 2D, 2.5D, and 3D segmentation networks for mandibular canals in CBCT images: a study on public and external datasets BMC Oral Health Deep learning CBCT image Mandibular canal Image segmentation 3D segmentation network |
| title | Comparison of 2D, 2.5D, and 3D segmentation networks for mandibular canals in CBCT images: a study on public and external datasets |
| title_full | Comparison of 2D, 2.5D, and 3D segmentation networks for mandibular canals in CBCT images: a study on public and external datasets |
| title_fullStr | Comparison of 2D, 2.5D, and 3D segmentation networks for mandibular canals in CBCT images: a study on public and external datasets |
| title_full_unstemmed | Comparison of 2D, 2.5D, and 3D segmentation networks for mandibular canals in CBCT images: a study on public and external datasets |
| title_short | Comparison of 2D, 2.5D, and 3D segmentation networks for mandibular canals in CBCT images: a study on public and external datasets |
| title_sort | comparison of 2d 2 5d and 3d segmentation networks for mandibular canals in cbct images a study on public and external datasets |
| topic | Deep learning CBCT image Mandibular canal Image segmentation 3D segmentation network |
| url | https://doi.org/10.1186/s12903-025-06483-4 |
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