ArticleJournal of applied clinical medical physics2022
Fully automated segmentation of clinical target volume in cervical cancer from magnetic resonance imaging with convolutional neural network.
Article in Journal of applied clinical medical physics, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 25 papers, 1 of them a synthesis that pooled it.
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Who cites it
25 citing papers in PubMed, 1 synthesis or guideline pooled it, 30 citations in OpenAlex.
- Deep learning in MRI-guided radiation therapy: A systematic review.Journal of applied clinical medical physics · 2024Pooled it
- Deep Learning and Machine Learning Algorithms for Cervical Cancer Segmentation on MRI: A Systematic Review.Journal of medical radiation sciences · 2026Review
- A lightweight deep learning model for automated segmentation of Gynecologic organs and cervical Tumors on T2-weighted magnetic resonance imaging.Physics and imaging in radiation oncology · 2026Article
- HAR-Net: Automatic delineation of clinical target volumes for cervical cancer guided by hierarchical anatomical refinement.Zhong nan da xue xue bao. Yi xue ban = Journal of Central South University. Medical sciences · 2026Article
- Systematic review of artificial intelligence in brachytherapy.Journal of contemporary brachytherapy · 2026Review
- Cervical cancer in the modern era: cutting-edge strategies for diagnosis and treatment.Japanese journal of radiology · 2026Review
- An auxiliary diagnosis model for the pathological classification of cervical cancer based on radiomics biomarkers.Frontiers in genetics · 2026Article
- Application of TransUnet Deep Learning Model for Automatic Segmentation of Cervical Cancer in Small-Field T2WI Images.Journal of imaging informatics in medicine · 2025Article
- Large-scale convolutional neural network for clinical target and multi-organ segmentation in gynecologic brachytherapy via multi-stage learning.Medical physics · 2025Article
- A review of artificial intelligence in brachytherapy.Journal of applied clinical medical physics · 2025Review
- Cer-ConvN3Unet: an end-to-end multi-parametric MRI-based pipeline for automated detection and segmentation of cervical cancer.European radiology experimental · 2025Article
- Artificial intelligence uncertainty quantification in radiotherapy applications - A scoping review.Radiotherapy and oncology : journal of the European Society for Therapeutic Radiology and Oncology · 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
- Article
- Cervical cancer segmentation based on medical images: a literature review.Quantitative imaging in medicine and surgery · 2024Review
- Artificial Intelligence Uncertainty Quantification in Radiotherapy Applications - A Scoping Review.medRxiv : the preprint server for health sciences · 2024Article
- Review of Deep Learning Based Autosegmentation for Clinical Target Volume: Current Status and Future Directions.Advances in radiation oncology · 2024Review
- Automatic gross tumor volume segmentation with failure detection for safe implementation in locally advanced cervical cancer.Physics and imaging in radiation oncology · 2024Article
- Deep Learning Framework with Multi-Head Dilated Encoders for Enhanced Segmentation of Cervical Cancer on Multiparametric Magnetic Resonance Imaging.Diagnostics (Basel, Switzerland) · 2023Article
- A network score-based metric to optimize the quality assurance of automatic radiotherapy target segmentations.Physics and imaging in radiation oncology · 2023Article
Corrections and comments
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Authors and funding
4 authors at 2 institutions in 1 country.
Funding
Abstract
purposeContouring clinical target volume (CTV) from medical images is an essential step for radiotherapy (RT) planning. Magnetic resonance imaging (MRI) is used as a standard imaging modality for CTV segmentation in cervical cancer due to its superior soft-tissue contrast. However, the delineation of CTV is challenging as CTV contains microscopic extensions that are not clearly visible even in MR images, resulting in significant contour variability among radiation oncologists depending on their knowledge and experience. In this study, we propose a fully automated deep learning-based method to segment CTV from MR images.
methodsOur method begins with the bladder segmentation, from which the CTV position is estimated in the axial view. The superior-inferior CTV span is then detected using an Attention U-Net. A CTV-specific region of interest (ROI) is determined, and three-dimensional (3-D) blocks are extracted from the ROI volume. Finally, a CTV segmentation map is computed using a 3-D U-Net from the extracted 3-D blocks.
resultsWe developed and evaluated our method using 213 MRI scans obtained from 125 patients (183 for training, 30 for test). Our method achieved (mean ± SD) Dice similarity coefficient of 0.85 ± 0.03 and the 95th percentile Hausdorff distance of 3.70 ± 0.35 mm on test cases, outperforming other state-of-the-art methods significantly (p-value < 0.05). Our method also produces an uncertainty map along with the CTV segmentation by employing the Monte Carlo dropout technique to draw physician's attention to the regions with high uncertainty, where careful review and manual correction may be needed.
conclusionsExperimental results show that the developed method is accurate, fast, and reproducible for contouring CTV from MRI, demonstrating its potential to assist radiation oncologists in alleviating the burden of tedious contouring for RT planning in cervical cancer.
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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.