ReviewEuropean journal of nuclear medicine and molecular imaging2010
PET-guided delineation of radiation therapy treatment volumes: a survey of image segmentation techniques.
Review in European journal of nuclear medicine and molecular imaging, 2010. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 114 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.
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Who cites it
114 citing papers in PubMed, 1 synthesis or guideline pooled it, 365 citations in OpenAlex.
- Recommendations for the use of PET and PET-CT for radiotherapy planning in research projects.The British journal of radiology · 2012Guideline
- Correlation between tumour characteristics, SUV measurements, metabolic tumour volume, TLG and textural features assessed withEuropean journal of nuclear medicine and molecular imaging · 2017Trial
- Robustness of Radiomic Features in [Molecular imaging and biology · 2016Trial
- Multimodality imaging with CT, MR and FDG-PET for radiotherapy target volume delineation in oropharyngeal squamous cell carcinoma.BMC cancer · 2015Trial
- Enhancing PET/CT Radiomics Robustness Through Graph Signal Processing.Diagnostics (Basel, Switzerland) · 2026Article
- NHOC/NHOP as novel biomarkers for predicting lymph node metastasis in NSCLC using PET/CT radiomics and machine learning: a two-center retrospective study.European journal of nuclear medicine and molecular imaging · 2026Article
- PAM: a propagation-based model for segmenting any 3D objects across multi-modal medical images.NPJ digital medicine · 2025Article
- Automated segmentation of the primary tumor in nasopharyngeal carcinoma using a deep learning framework in positron emission tomography imaging: a comparative study.Quantitative imaging in medicine and surgery · 2025Article
- The Role of PET Image Quantification in Modern Radiation Therapy.PET clinics · 2025Review
- Supporting intraoperative margin assessment using deep learning for automatic tumour segmentation in breast lumpectomy micro-PET-CT.NPJ breast cancer · 2025Article
- Generative AI enables medical image segmentation in ultra low-data regimes.Nature communications · 2025Article
- Article
- 3D printing of radioactive wall-less PET phantoms improves threshold-based target delineation and quantification.EJNMMI physics · 2025Article
- Comparison ofScientific reports · 2025Article
- Deep Learning-Powered CT-Less Multitracer Organ Segmentation From PET Images: A Solution for Unreliable CT Segmentation in PET/CT Imaging.Clinical nuclear medicine · 2025Article
- PET radiomics in lung cancer: advances and translational challenges.EJNMMI physics · 2024Review
- Semi-supervised learning towards automated segmentation of PET images with limited annotations: application to lymphoma patients.Physical and engineering sciences in medicine · 2024Article
- Evaluation of Semiautomatic and Deep Learning-Based Fully Automatic Segmentation Methods on [Journal of digital imaging · 2023Article
- Role of 18F-choline and 18F-fluorodeoxyglucose positron emission tomography in combination with magnetic resonance imaging in brachytherapy planning for locally advanced cervical cancer: A pilot study.Physics and imaging in radiation oncology · 2023Article
- Automated detection, delineation and quantification of whole-body bone metastasis using FDG-PET/CT images.Physical and engineering sciences in medicine · 2023Article
54 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
2 authors at 2 institutions in 2 countries.
Funding
Abstract
Historically, anatomical CT and MR images were used to delineate the gross tumour volumes (GTVs) for radiotherapy treatment planning. The capabilities offered by modern radiation therapy units and the widespread availability of combined PET/CT scanners stimulated the development of biological PET imaging-guided radiation therapy treatment planning with the aim to produce highly conformal radiation dose distribution to the tumour. One of the most difficult issues facing PET-based treatment planning is the accurate delineation of target regions from typical blurred and noisy functional images. The major problems encountered are image segmentation and imperfect system response function. Image segmentation is defined as the process of classifying the voxels of an image into a set of distinct classes. The difficulty in PET image segmentation is compounded by the low spatial resolution and high noise characteristics of PET images. Despite the difficulties and known limitations, several image segmentation approaches have been proposed and used in the clinical setting including thresholding, edge detection, region growing, clustering, stochastic models, deformable models, classifiers and several other approaches. A detailed description of the various approaches proposed in the literature is reviewed. Moreover, we also briefly discuss some important considerations and limitations of the widely used techniques to guide practitioners in the field of radiation oncology. The strategies followed for validation and comparative assessment of various PET segmentation approaches are described. Future opportunities and the current challenges facing the adoption of PET-guided delineation of target volumes and its role in basic and clinical research are also addressed.
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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.