ReviewComputers in biology and medicine2014
A review on segmentation of positron emission tomography images.
Review in Computers in biology and medicine, 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 129 papers.
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
129 citing papers in PubMed.
- Challenges in tracer development for tumor microenvironment (TME) imaging.European journal of nuclear medicine and molecular imaging · 2026Review
- Automatic computation of breast cancer biomarkers from multiple [Formula: see text] F-FDG PET image segmentation.Scientific reports · 2026Article
- Digital versus analogue PET in parathyroid imaging: comparison of PET metrics and machine learning-based characterisation of hyperfunctioning lesions (the DIGI-PET study).European journal of nuclear medicine and molecular imaging · 2026Article
- Cerebral glucose metabolism: the potential missing link in autism research-a literature review and meta-analytic synthesis.Journal of neural transmission (Vienna, Austria : 1996) · 2026Review
- 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
- Integrated PET-IVIM-DKI MRI for predicting lymphovascular invasion in NSCLC.Insights into imaging · 2025Article
- 3D printing of radioactive wall-less PET phantoms improves threshold-based target delineation and quantification.EJNMMI physics · 2025Article
- Value of PET radiomic features for diagnosis and reccurence prediction of newly diagnosed oral squamous cell carcinoma.Scientific reports · 2025Article
- Robustness of textural analysis features in quantitativeEJNMMI physics · 2025Article
- Organ-specific response to [Endocrine · 2025Article
- Ensemble Deep Learning Models for Automated Segmentation of Tumor and Lymph Node Volumes in Head and Neck Cancer Using Pre- and Mid-Treatment MRI: Application of Auto3DSeg and SegResNet.Head and Neck Tumor Segmentation for MR-Guided Applications : First MICCAI Challenge, HNTS-MRG 2024, held in conjunction with MICCAI 2024, Marrakesh, Morocco, October 17, 2024, proceedings · 2025Article
- PET radiomics in lung cancer: advances and translational challenges.EJNMMI physics · 2024Review
- Nuclear-Based Labeling of Cellular Immunotherapies: A Simple Protocol for Preclinical Use.Molecular imaging and biology · 2024Article
- Why do patients with cancer die?Nature reviews. Cancer · 2024Review
- Accuracy of gross tumour volume delineation with [68Ga]-PSMA-PET compared to histopathology for high-risk prostate cancer.Acta oncologica (Stockholm, Sweden) · 2024Article
- Hepatic glucose metabolism in the steatotic liver.Nature reviews. Gastroenterology & hepatology · 2024Review
- Dynamic total-body PET/CT imaging with reduced acquisition time shows acceptable performance in quantification of [European journal of nuclear medicine and molecular imaging · 2024Article
- Need for Objective Task-Based Evaluation of Image Segmentation Algorithms for Quantitative PET: A Study with ACRIN 6668/RTOG 0235 Multicenter Clinical Trial Data.Journal of nuclear medicine : official publication, Society of Nuclear Medicine · 2024Article
- The Challenge of Single-Photon Emission Computed Tomography Image Segmentation in the Internal Dosimetry ofJournal of imaging · 2024Review
- A ROI-based quantitative pipeline forHeliyon · 2024Article
69 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
5 authors.
Funding
Abstract
Positron Emission Tomography (PET), a non-invasive functional imaging method at the molecular level, images the distribution of biologically targeted radiotracers with high sensitivity. PET imaging provides detailed quantitative information about many diseases and is often used to evaluate inflammation, infection, and cancer by detecting emitted photons from a radiotracer localized to abnormal cells. In order to differentiate abnormal tissue from surrounding areas in PET images, image segmentation methods play a vital role; therefore, accurate image segmentation is often necessary for proper disease detection, diagnosis, treatment planning, and follow-ups. In this review paper, we present state-of-the-art PET image segmentation methods, as well as the recent advances in image segmentation techniques. In order to make this manuscript self-contained, we also briefly explain the fundamentals of PET imaging, the challenges of diagnostic PET image analysis, and the effects of these challenges on the segmentation results.
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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.