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Comparison of 2D, 2.5D, and 3D segmentation networks for maxillary sinuses and lesions in CBCT images
DC Field | Value | Language |
---|---|---|
dc.contributor.author | Yoo, Yeon-Sun | - |
dc.contributor.author | Kim, DaEl | - |
dc.contributor.author | Yang, Su | - |
dc.contributor.author | Kang, Se-Ryong | - |
dc.contributor.author | Kim, Jo-Eun | - |
dc.contributor.author | Huh, Kyung-Hoe | - |
dc.contributor.author | Lee, Sam-Sun | - |
dc.contributor.author | Heo, Min-Suk | - |
dc.contributor.author | Yi, Won-Jin | - |
dc.date.accessioned | 2023-11-20T02:15:38Z | - |
dc.date.available | 2023-11-20T11:16:50Z | - |
dc.date.issued | 2023-11-15 | - |
dc.identifier.citation | BMC Oral Health, Vol.23(1):866 | ko_KR |
dc.identifier.issn | 1472-6831 | - |
dc.identifier.uri | https://hdl.handle.net/10371/196142 | - |
dc.description.abstract | Background
The purpose of this study was to compare the segmentation performances of the 2D, 2.5D, and 3D networks for maxillary sinuses (MSs) and lesions inside the maxillary sinus (MSL) with variations in sizes, shapes, and locations in cone beam CT (CBCT) images under the same constraint of memory capacity. Methods The 2D, 2.5D, and 3D networks were compared comprehensively for the segmentation of the MS and MSL in CBCT images under the same constraint of memory capacity. MSLs were obtained by subtracting the prediction of the air region of the maxillary sinus (MSA) from that of the MS. Results The 2.5D network showed the highest segmentation performances for the MS and MSA compared to the 2D and 3D networks. The performances of the Jaccard coefficient, Dice similarity coefficient, precision, and recall by the 2.5D network of U-net + + reached 0.947, 0.973, 0.974, and 0.971 for the MS, respectively, and 0.787, 0.875, 0.897, and 0.858 for the MSL, respectively. Conclusions The 2.5D segmentation network demonstrated superior segmentation performance for various MSLs with an ensemble learning approach of combining the predictions from three orthogonal planes. | ko_KR |
dc.description.sponsorship | This work was supported by the Korea Medical Device Development Fund grant funded by the Korean government (the Ministry of Science and ICT, the Ministry of Trade, Industry and Energy, the Ministry of Health & Welfare, the Ministry of Food and Drug Safety) (Project Number: 1711174552, KMDF_PR_20200901_0147) (Project Number: 1711194231, KMDF_PR_20200901_0011). This work was supported by a National Research Foundation of Korea (NRF) grant funded by the Korean government (MSIT) (No.2023R1A2C200532611) | ko_KR |
dc.language.iso | en | ko_KR |
dc.publisher | BMC | ko_KR |
dc.subject | Deep learning | - |
dc.subject | CBCT image | - |
dc.subject | Maxillary sinus segmentation | - |
dc.subject | Maxillary sinus lesion segmentation | - |
dc.subject | 2.5D network | - |
dc.title | Comparison of 2D, 2.5D, and 3D segmentation networks for maxillary sinuses and lesions in CBCT images | ko_KR |
dc.type | Article | ko_KR |
dc.identifier.doi | 10.1186/s12903-023-03607-6 | ko_KR |
dc.citation.journaltitle | BMC Oral Health | ko_KR |
dc.language.rfc3066 | en | - |
dc.rights.holder | The Author(s) | - |
dc.date.updated | 2023-11-19T04:53:37Z | - |
dc.citation.endpage | 14 | ko_KR |
dc.citation.number | 1 | ko_KR |
dc.citation.startpage | 1 | ko_KR |
dc.citation.volume | 23 | ko_KR |
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