A deep learning‐based 3D Prompt‐nnUnet model for automatic segmentation in brachytherapy of postoperative endometrial carcinoma
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Le résumé fourni par la source
PURPOSE: To create and evaluate a three-dimensional (3D) Prompt-nnUnet module that utilizes the prompts-based model combined with 3D nnUnet for producing the rapid and consistent autosegmentation of high-risk clinical target volume (HR CTV) and organ at risk (OAR) in high-dose-rate brachytherapy (HDR BT) for patients with postoperative endometrial carcinoma (EC). METHODS AND MATERIALS: On two experimental batches, a total of 321 computed tomography (CT) scans were obtained for HR CTV segmentation from 321 patients with EC, and 125 CT scans for OARs segmentation from 125 patients. The numbers of training/validation/test were 257/32/32 and 87/13/25 for HR CTV and OARs respectively. A novel comparison of the deep learning neural network 3D Prompt-nnUnet and 3D nnUnet was applied for HR CTV and OARs segmentation. Three-fold cross validation and several quantitative metrics were employed, including Dice similarity coefficient (DSC), Hausdorff distance (HD), 95th percentile of Hausdorff distance (HD95%), and intersection over union (IoU). RESULTS: The Prompt-nnUnet included two forms of parameters Predict-Prompt (PP) and Label-Prompt (LP), with the LP performing most similarly to the experienced radiation oncologist and outperforming the less experienced ones. During the testing phase, the mean DSC values for the LP were 0.96 ± 0.02, 0.91 ± 0.02, and 0.83 ± 0.07 for HR CTV, rectum and urethra, respectively. The mean HD values (mm) were 2.73 ± 0.95, 8.18 ± 4.84, and 2.11 ± 0.50, respectively. The mean HD95% values (mm) were 1.66 ± 1.11, 3.07 ± 0.94, and 1.35 ± 0.55, respectively. The mean IoUs were 0.92 ± 0.04, 0.84 ± 0.03, and 0.71 ± 0.09, respectively. A delineation time < 2.35 s per structure in the new model was observed, which was available to save clinician time. CONCLUSION: The Prompt-nnUnet architecture, particularly the LP, was highly consistent with ground truth (GT) in HR CTV or OAR autosegmentation, reducing interobserver variability and shortening treatment time.
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Le contrôle bibliographique ouvert
DOI retrouvé dans Crossref DOI retrouvé ; titre concordant.
- Titre Crossref
- A deep learning‐based 3D Prompt‐nnUnet model for automatic segmentation in brachytherapy of postoperative endometrial carcinoma
- Date Crossref
- 29/04/2024
- Éditeur
- Wiley
- Type
- journal-article
Ce recoupement confirme des métadonnées liées au DOI. Il ne confirme ni la méthode ni les conclusions de l’étude, et il ne compte pas comme une seconde source scientifique indépendante.
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National Institute for Radiological Protection pays non établi dans la noticeOrganisme public
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Digital China Health (China) pays non établi dans la noticeEntreprise
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Peking University Department of Radiotherapy pays non établi dans la noticeUniversité ou école supérieure
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Peking University Third Hospital pays non établi dans la noticeÉtablissement de santé
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Chinese PLA General Hospital pays non établi dans la noticeÉtablissement de santé
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Chinese General Hospital College of Nursing and Liberal Arts pays non établi dans la noticeÉtablissement de santé
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People's Liberation Army No. 150 Hospital pays non établi dans la noticeÉtablissement de santé
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Chinese People's Liberation Army Department of Radiotherapy pays non établi dans la noticeOrganisme public
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Space Engineering University pays non établi dans la noticeUniversité ou école supérieure
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Beihang University Department of Aero-space Information Engineering pays non établi dans la noticeUniversité ou école supérieure
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Secondary Standard Dosimetry Laboratory pays non établi dans la noticeStructure de recherche
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LTD Beijing China Digital Health China Technologies Co. pays non établi dans la noticeEntreprise
National Institute for Radiological Protection, Digital China Health (China) et Department of Radiotherapy — Peking University, avec 9 autres affiliations.
Une affiliation ne permet pas de déduire la nationalité d’un auteur.