ArticleJournal of applied clinical medical physics2023
Self-configuring nnU-Net for automatic delineation of the organs at risk and target in high-dose rate cervical brachytherapy, a low/middle-income country's experience.
Article in Journal of applied clinical medical physics, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers.
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Who cites it
15 citing papers in PubMed, 14 citations in OpenAlex.
- Deep learning auto-contouring of target and organs-at-risk on post-catheter implant CT images for prostate HDR brachytherapy.Journal of applied clinical medical physics · 2026Article
- Robust framework for adaptive field-of-view automatic segmentation in vaginal brachytherapy for endometrial cancer.Physics and imaging in radiation oncology · 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
- Artificial intelligence-assisted radiotherapy for pelvic and abdominal malignancies: assessing feasibility in the context of Africa-specific risks.Frontiers in oncology · 2026Review
- Large-scale convolutional neural network for clinical target and multi-organ segmentation in gynecologic brachytherapy via multi-stage learning.Medical physics · 2025Article
- Deep learning-based applicator selection between Syed and T&O in high-dose-rate brachytherapy for locally advanced cervical cancer: a retrospective study.Physics in medicine and biology · 2025Article
- A review of artificial intelligence in brachytherapy.Journal of applied clinical medical physics · 2025Review
- Open-source deep-learning models for segmentation of normal structures for prostatic and gynecological high-dose-rate brachytherapy: Comparison of architectures.Journal of applied clinical medical physics · 2025Article
- Clinical target volume (CTV) automatic delineation using deep learning network for cervical cancer radiotherapy: A study with external validation.Journal of applied clinical medical physics · 2025Article
- Artificial intelligence in interventional radiotherapy (brachytherapy): Enhancing patient-centered care and addressing patients' needs.Clinical and translational radiation oncology · 2024Review
- Article
- Self-configuring nnU-Net for automatic delineation of the organs at risk and target in high-dose rate cervical brachytherapy, a low/middle-income country's experience.Journal of applied clinical medical physics · 2023Article
- A Lightweight Skeletal Muscle Intelligent Segmentation Network Based on Planning CT for Cervical Cancer Radiotherapy.Technology in cancer research & treatmentArticle
- C2FAU-Net: A Deep Learning Approach with Multi-scale Strategy for Automated Delineation of Organs-at-risk in Cervical Cancer High-dose Rate Brachytherapy.Journal of medical physicsArticle
Corrections and comments
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Authors and funding
4 authors at 3 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundThe high-dose rate (HDR) brachytherapy treatment planning workflow for cervical cancer is a labor-intensive, time-consuming, and expertise-driven process. These issues are amplified in low/middle-income countries with large deficits in experienced healthcare professionals. Automation has the ability to substantially reduce bottlenecks in the planning process but often require a high level of expertise to develop. PURPOSE: To implement the out of the box self-configuring nnU-Net package for the auto-segmentation of the organs at risk (OARs) and high-risk CTV (HR CTV) for Ring-Tandem (R-T) HDR cervical brachytherapy treatment planning.
methodsThe computed tomography (CT) scans of 100 previously treated patients were used to train and test three different nnU-Net configurations (2D, 3DFR, and 3DCasc). The performance of the models was evaluated by calculating the Sørensen-dice similarity coefficient, Hausdorff distance (HD), 95
resultsThe mean DSC, HD, HD95, MSD and precision scores for our best performing model (3DFR) were 0.92/7.5 mm/3.0 mm/ 0.8 mm/0.91 for the bladder, 0.84/13.8 mm/5.3 mm/1.4 mm/0.84 for the rectum, and 0.81/8.5 mm/6.0 mm/2.2 mm/0.80 for the HR CTV. Mean dose differences (D
conclusionOur best performing model (3DFR) provided fast accurate auto generated OARs and HR CTV contours with a large clinical acceptance rate.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.