ArticleAnnals of translational medicine2021
RefineNet-based automatic delineation of the clinical target volume and organs at risk for three-dimensional brachytherapy for cervical cancer.
Article in Annals of translational medicine, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 19 papers.
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19 citing papers in PubMed.
- Segmentation for pelvic malignancies in radiation oncology practice: a systematic review and meta-analysis protocol.Systematic reviews · 2026Article
- Deep learning-based auto-contouring for organs at risk in three-dimensional image-guided brachytherapy for cervical cancer and endometrial cancer.Journal of applied clinical medical physics · 2026Article
- Systematic review of artificial intelligence in brachytherapy.Journal of contemporary brachytherapy · 2026Review
- Advances in proton therapy technology and global clinical applications.Frontiers in oncology · 2026Review
- Prior knowledge of anatomical relationships supports automatic delineation of clinical target volume for cervical cancer.Scientific reports · 2025Article
- A review of artificial intelligence in brachytherapy.Journal of applied clinical medical physics · 2025Review
- A deep learning model based on Mamba for automatic segmentation in cervical cancer brachytherapy.Scientific reports · 2025Article
- Artificial intelligence in high-dose-rate brachytherapy treatment planning for cervical cancer: a review.Frontiers in oncology · 2025Review
- Artificial intelligence in interventional radiotherapy (brachytherapy): Enhancing patient-centered care and addressing patients' needs.Clinical and translational radiation oncology · 2024Review
- Article
- A deep learning-based 3D Prompt-nnUnet model for automatic segmentation in brachytherapy of postoperative endometrial carcinoma.Journal of applied clinical medical physics · 2024Article
- Cervical cancer segmentation based on medical images: a literature review.Quantitative imaging in medicine and surgery · 2024Review
- The use of deep learning in interventional radiotherapy (brachytherapy): A review with a focus on open source and open data.Zeitschrift fur medizinische Physik · 2024Review
- Review of Deep Learning Based Autosegmentation for Clinical Target Volume: Current Status and Future Directions.Advances in radiation oncology · 2024Review
- Reliable Delineation of Clinical Target Volumes for Cervical Cancer Radiotherapy on CT/MR Dual-Modality Images.Journal of imaging informatics in medicine · 2024Article
- Prospective Evaluation of Automated Contouring for CT-Based Brachytherapy for Gynecologic Malignancies.Advances in radiation oncology · 2024Article
- Automated treatment planning framework for brachytherapy of cervical cancer using 3D dose predictions.Physics in medicine and biology · 2023Article
- Automatic reconstruction of interstitial needles using CT images in post-operative cervical cancer brachytherapy based on deep learning.Journal of contemporary brachytherapy · 2023Article
- Ensemble Learning for Three-dimensional Medical Image Segmentation of Organ at Risk in Brachytherapy Using Double U-Net, Bi-directional ConvLSTM U-Net, and Transformer Network.Journal of medical physicsArticle
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4 authors.
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Abstract
backgroundIn recent years, high-precision image-guided intensity-modulated radiation therapy combined with three-dimensional (3D) high-dose-rate (HDR) brachytherapy (BT) has become a recommended technique for radical radiotherapy for cervical cancer. This study first employed contrast-limited adaptive histogram equalization (CLAHE) for preprocessing of input data to achieve image enhancement. In this way, rapid and accurate automatic delineation of the clinical target volume (CTV) and organs at risk (OARs) in 3D BT for cervical cancer was achieved.
methodsTwo hundred cervical cancer patients who underwent radical radiotherapy from January 2016 to December 2018 were selected. After collecting the computed tomography (CT) image data of a patient, we constructed the radiotherapy CTV and OAR image libraries. A RefineNet-based deep learning protocol was used to segment the CTV and OARs for 3D BT for cervical cancer. In this study, a total of 1,000 rounds of training were carried out, and the model with the best performance was selected for subsequent iterative tuning. Finally, the clinical test was carried out, in which the CT images of 10 cases were tested one by one. The manual delineation results and the model output results for the CTV and OARs were compared to measure the performance of the model.
resultsCompared with the manually delineated CTV, the RefineNet model-based segmented CTV had a higher Dice similarity coefficient (DSC), Hausdorff distance (HD), and overlap index (OI), which were 0.861, 6.005, and 0.839, respectively. For OARs, the RefineNet-based model obtained the best results for bladder segmentation (DSC: 85.96%), respectively. The mean duration of RefineNet-based automatic contour processing of the CTV was 70 s, and the mean durations of RefineNet-based automatic delineation of the bladder, rectum, sigmoid colon, and small intestine were 67, 67.4, 63.8, and 60.8 s, respectively. The total time saved by RefineNet was approximately 60%.
conclusionsThe RefineNet-based automatic delineation model for 3D BT for cervical cancer is a stable and highly consistent automatic delineation algorithmic model that has the potential to improve the consistency of target region delineation, simplify the radiotherapy procedure, and achieve rapid and accurate automatic delineation of CTVs and OARs.
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