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| Format: | Artículo Open Access |
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Wiley
2024
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| Online Access: | https://aao-hnsfjournals.onlinelibrary.wiley.com/doi/10.1002/ohn.764 |
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| author | Caio A. Neves Trishia El. Chemaly Fanrui Fu Nikolas H. Blevins |
| author_facet | Caio A. Neves Trishia El. Chemaly Fanrui Fu Nikolas H. Blevins Caio A. Neves Trishia El. Chemaly Fanrui Fu Nikolas H. Blevins |
| collection | Wiley Open Access |
| contents | Deep Learning Method for Rapid Simultaneous Multistructure Temporal Bone Segmentation Caio A. Neves Trishia El. Chemaly Fanrui Fu Nikolas H. Blevins Otolaryngology–Head and Neck Surgery AbstractObjectiveTo develop and validate a deep learning algorithm for the automated segmentation of key temporal bone structures from clinical computed tomography (CT) data sets.Study DesignCross‐sectional study.SettingA total of 325 CT scans from a clinical database.MethodA state‐of‐the‐art deep learning (DL) algorithm (SwinUNETR) was used to train a prediction model for rapid segmentation of 9 key temporal bone structures in a data set of 325 clinical CTs. The data set was manually annotated by a specialist to serve as the ground truth. The data set was randomly split into training (n = 260) and testing (n = 65) sets. The model's performance was objectively assessed through external validation on the test set using metrics including Dice, Balanced accuracy, Hausdorff distances, and processing time.ResultsThe model achieved an average Dice coefficient of 0.87 for all structures, an average balanced accuracy of 0.94, an average Hausdorff distance of 0.79 mm, and an average processing time of 9.1 seconds per CT.ConclusionThe present DL model for the automated simultaneous segmentation of multiple structures within the temporal bone from CTs achieved high accuracy according to currently commonly employed objective analysis. The results demonstrate the potential of the method to improve preoperative evaluation and intraoperative guidance in otologic surgery. 10.1002/ohn.764 http://onlinelibrary.wiley.com/termsAndConditions#vor |
| doi_str_mv | 10.1002/ohn.764 |
| format | Artículo Open Access |
| id | wiley_oa_10_1002_ohn_764 |
| institution | Wiley Open Access |
| license_str_mv | http://onlinelibrary.wiley.com/termsAndConditions#vor |
| publishDate | 2024 |
| publisher | Wiley |
| record_format | wiley_oa |
| spellingShingle | Deep Learning Method for Rapid Simultaneous Multistructure Temporal Bone Segmentation Caio A. Neves Trishia El. Chemaly Fanrui Fu Nikolas H. Blevins Otolaryngology–Head and Neck Surgery Deep Learning Method for Rapid Simultaneous Multistructure Temporal Bone Segmentation Caio A. Neves Trishia El. Chemaly Fanrui Fu Nikolas H. Blevins Otolaryngology–Head and Neck Surgery AbstractObjectiveTo develop and validate a deep learning algorithm for the automated segmentation of key temporal bone structures from clinical computed tomography (CT) data sets.Study DesignCross‐sectional study.SettingA total of 325 CT scans from a clinical database.MethodA state‐of‐the‐art deep learning (DL) algorithm (SwinUNETR) was used to train a prediction model for rapid segmentation of 9 key temporal bone structures in a data set of 325 clinical CTs. The data set was manually annotated by a specialist to serve as the ground truth. The data set was randomly split into training (n = 260) and testing (n = 65) sets. The model's performance was objectively assessed through external validation on the test set using metrics including Dice, Balanced accuracy, Hausdorff distances, and processing time.ResultsThe model achieved an average Dice coefficient of 0.87 for all structures, an average balanced accuracy of 0.94, an average Hausdorff distance of 0.79 mm, and an average processing time of 9.1 seconds per CT.ConclusionThe present DL model for the automated simultaneous segmentation of multiple structures within the temporal bone from CTs achieved high accuracy according to currently commonly employed objective analysis. The results demonstrate the potential of the method to improve preoperative evaluation and intraoperative guidance in otologic surgery. 10.1002/ohn.764 http://onlinelibrary.wiley.com/termsAndConditions#vor |
| title | Deep Learning Method for Rapid Simultaneous Multistructure Temporal Bone Segmentation |
| topic | Otolaryngology–Head and Neck Surgery |
| url | https://aao-hnsfjournals.onlinelibrary.wiley.com/doi/10.1002/ohn.764 |