Publications by authors named "Laiyuan Tao"
To develop and evaluate a 3D Prompt-ResUNet module that utilized the prompt-based model combined with 3D nnUNet for rapid and consistent autosegmentation of high-risk clinical target volume (HRCTV) and organ at risk (OAR) in high-dose-rate brachytherapy for cervical cancer patients.We used 73 computed tomography scans and 62 magnetic resonance imaging scans from 135 (103 for training, 16 for validation, and 16 for testing) cervical cancer patients across two hospitals for HRCTV and OAR segmentation. A novel comparison of the deep learning neural networks 3D Prompt-ResUNet, nnUNet, and segment anything model-Med3D was applied for the segmentation.
View Article and Find Full Text PDFArticle Synopsis
- Developed a 3D Prompt-nnUnet module to enhance autosegmentation of high-risk clinical areas in endometrial carcinoma brachytherapy.
- Utilized a large dataset of CT scans from 321 patients for HR CTV and 125 patients for OAR segmentation, with rigorous validation methods including cross-validation and multiple performance metrics.
- Results showed the Prompt-nnUnet, especially using Label-Prompt, achieved high accuracy and speed in segmentation, potentially saving significant time for clinicians compared to traditional methods.