ReviewMedical physics2014
Vision 20/20: perspectives on automated image segmentation for radiotherapy.
Review in Medical physics, 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 171 papers, 1 of them a synthesis that pooled it.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
171 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Deep learning in pulmonary nodule detection and segmentation: a systematic review.European radiology · 2025Pooled it
- The impact of robustness of deformable image registration on contour propagation and dose accumulation for head and neck adaptive radiotherapy.Journal of applied clinical medical physics · 2018Trial
- The feasibility of atlas-based automatic segmentation of MRI for H&N radiotherapy planning.Journal of applied clinical medical physics · 2016Trial
- Practical deep learning solutions for prostate cancer segmentation and implementation in radiotherapy planning.Physical and engineering sciences in medicine · 2026Article
- Deep Learning and Machine Learning Algorithms for Cervical Cancer Segmentation on MRI: A Systematic Review.Journal of medical radiation sciences · 2026Review
- 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
- FS-Mamba: Feature-wise scanning Mamba UNet for automatic image segmentation in liver tumor radiotherapy.Journal of applied clinical medical physics · 2026Article
- AI-Based Dose Compliance of Secondary Organs at Risk in Head and Neck Cancer Radiotherapy.Diagnostics (Basel, Switzerland) · 2026Article
- Evaluation of efficacy in prostate cancer contouring for MR-guided radiotherapy using structured contouring training modules.Technical innovations & patient support in radiation oncology · 2026Article
- A comparative evaluation of commercial software programs for automatic segmentation in breast cancer radiotherapy planning.Radiological physics and technology · 2026Article
- Atlas selection methods for multi-atlas-based segmentation in breast cancer radiotherapy planning.Radiological physics and technology · 2026Article
- Fractal-driven self-supervised learning enhances early-stage lung cancer GTV segmentation: a novel transfer learning framework.Japanese journal of radiology · 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
- Data annotation and its evaluation in artificial intelligence-based anatomy recognition for ultrasound-guided regional anesthesia: a clinical perspective.Frontiers in medicine · 2026Article
- Geometric evaluation of a deep learning method for segmentation of urinary OARs on magnetic resonance imaging for prostate cancer radiotherapy.Clinical and translational radiation oncology · 2026Article
- Deep CNN-based Fully Automated Segmentation of Pelvic Multi-Organ on CT Images for Prostate Cancer Radiotherapy.Journal of biomedical physics & engineering · 2025Article
- Trueness of artificial intelligence-based, manual, and global thresholding segmentation protocols for human mandibles.Journal of prosthodontics : official journal of the American College of Prosthodontists · 2025Article
- Long-term breast cancer risk post childhood cancer radiotherapy: data-driven screening recommendations for survivors.The British journal of radiology · 2025Article
- Evaluating the quality of multiple automatically produced segmentation variants of the prostate on Magnetic Resonance Imaging scans for brachytherapy.Physics and imaging in radiation oncology · 2025Article
- A dual-layer quality assurance approach leveraging dose prediction for efficient review of automated contours of organs at risk in the brain in radiotherapy.Physics and imaging in radiation oncology · 2025Article
111 more citing papers are in PubMed but not listed here.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
7 authors.
Funding
Abstract
Due to rapid advances in radiation therapy (RT), especially image guidance and treatment adaptation, a fast and accurate segmentation of medical images is a very important part of the treatment. Manual delineation of target volumes and organs at risk is still the standard routine for most clinics, even though it is time consuming and prone to intra- and interobserver variations. Automated segmentation methods seek to reduce delineation workload and unify the organ boundary definition. In this paper, the authors review the current autosegmentation methods particularly relevant for applications in RT. The authors outline the methods' strengths and limitations and propose strategies that could lead to wider acceptance of autosegmentation in routine clinical practice. The authors conclude that currently, autosegmentation technology in RT planning is an efficient tool for the clinicians to provide them with a good starting point for review and adjustment. Modern hardware platforms including GPUs allow most of the autosegmentation tasks to be done in a range of a few minutes. In the nearest future, improvements in CT-based autosegmentation tools will be achieved through standardization of imaging and contouring protocols. In the longer term, the authors expect a wider use of multimodality approaches and better understanding of correlation of imaging with biology and pathology.
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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.