ArticleMedical physics2020
Automatic contouring system for cervical cancer using convolutional neural networks.
Article in Medical physics, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 49 papers, 1 of them a synthesis that pooled it.
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Who cites it
49 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Deep learning in CT image segmentation of cervical cancer: a systematic review and meta-analysis.Radiation oncology (London, England) · 2022Pooled it
- Computed Tomographic Radiomics in Differentiating Histologic Subtypes of Epithelial Ovarian Carcinoma.JAMA network open · 2022Trial
- A prospective development and evaluation of a 2D convolutional neural network-based auto-segmentation model for cervical cancer radiotherapy.Journal of the Egyptian National Cancer Institute · 2026Article
- Segmentation for pelvic malignancies in radiation oncology practice: a systematic review and meta-analysis protocol.Systematic reviews · 2026Article
- Automatic pelvic bone segmentation for radiotherapy using diagnostic imaging workstation.Reports of practical oncology and radiotherapy : journal of Greatpoland Cancer Center in Poznan and Polish Society of Radiation Oncology · 2026Article
- Gross-tumor-volume segment-anything model for medical 2D images integrating gross tumor volume-minimal feature integration technology for lung cancer segmentation.Quantitative imaging in medicine and surgery · 2026Article
- Batch Export: An automated framework for curated data extraction via the Eclipse treatment planning system.Journal of applied clinical medical physics · 2025Article
- Machine and Deep Learning for the Diagnosis, Prognosis, and Treatment of Cervical Cancer: A Scoping Review.Diagnostics (Basel, Switzerland) · 2025Review
- Automatic cervical tumors segmentation in PET/MRI by parallel encoder U-net.Radiation oncology (London, England) · 2025Article
- RAU-Net for precise lung cancer GTV segmentation in radiation therapy planning.Scientific reports · 2025Article
- Diffusion-CSPAM U-Net: A U-Net model integrated hybrid attention mechanism and diffusion model for segmentation of computed tomography images of brain metastases.Radiation oncology (London, England) · 2025Article
- Automatic delineation of cervical cancer target volumes in small samples based on multi-decoder and semi-supervised learning and clinical application.Scientific reports · 2024Article
- Clinical evaluation of the convolutional neural network‑based automatic delineation tool in determining the clinical target volume and organs at risk in rectal cancer radiotherapy.Oncology letters · 2024Article
- Impact of annotation imperfections and auto-curation for deep learning-based organ-at-risk segmentation.Physics and imaging in radiation oncology · 2024Article
- 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
- Evaluating automatically generated normal tissue contours for safe use in head and neck and cervical cancer treatment planning.Journal of applied clinical medical physics · 2024Article
- Prospective Evaluation of Automated Contouring for CT-Based Brachytherapy for Gynecologic Malignancies.Advances in radiation oncology · 2024Article
- Multicenter Study of Pelvic Nodal Autosegmentation Algorithm of Siemens Healthineers: Comparison of Male Versus Female Pelvis.Advances in radiation oncology · 2024Article
- The accuracy of artificial intelligence deformed nodal structures in cervical online cone-beam-based adaptive radiotherapy.Physics and imaging in radiation oncology · 2024Article
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17 authors.
Funding
Abstract
purposeTo develop a tool for the automatic contouring of clinical treatment volumes (CTVs) and normal tissues for radiotherapy treatment planning in cervical cancer patients.
methodsAn auto-contouring tool based on convolutional neural networks (CNN) was developed to delineate three cervical CTVs and 11 normal structures (seven OARs, four bony structures) in cervical cancer treatment for use with the Radiation Planning Assistant, a web-based automatic plan generation system. A total of 2254 retrospective clinical computed tomography (CT) scans from a single cancer center and 210 CT scans from a segmentation challenge were used to train and validate the CNN-based auto-contouring tool. The accuracy of the tool was evaluated by calculating the Sørensen-dice similarity coefficient (DSC) and mean surface and Hausdorff distances between the automatically generated contours and physician-drawn contours on 140 internal CT scans. A radiation oncologist scored the automatically generated contours on 30 external CT scans from three South African hospitals.
resultsThe average DSC, mean surface distance, and Hausdorff distance of our CNN-based tool were 0.86/0.19 cm/2.02 cm for the primary CTV, 0.81/0.21 cm/2.09 cm for the nodal CTV, 0.76/0.27 cm/2.00 cm for the PAN CTV, 0.89/0.11 cm/1.07 cm for the bladder, 0.81/0.18 cm/1.66 cm for the rectum, 0.90/0.06 cm/0.65 cm for the spinal cord, 0.94/0.06 cm/0.60 cm for the left femur, 0.93/0.07 cm/0.66 cm for the right femur, 0.94/0.08 cm/0.76 cm for the left kidney, 0.95/0.07 cm/0.84 cm for the right kidney, 0.93/0.05 cm/1.06 cm for the pelvic bone, 0.91/0.07 cm/1.25 cm for the sacrum, 0.91/0.07 cm/0.53 cm for the L4 vertebral body, and 0.90/0.08 cm/0.68 cm for the L5 vertebral bodies. On average, 80% of the CTVs, 97% of the organ at risk, and 98% of the bony structure contours in the external test dataset were clinically acceptable based on physician review.
conclusionsOur CNN-based auto-contouring tool performed well on both internal and external datasets and had a high rate of clinical acceptability.
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