ArticleInternational journal of biomedical imaging2010
Artificial Neural Network-Based System for PET Volume Segmentation.
Article in International journal of biomedical imaging, 2010. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers.
What it found
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Who cites it
14 citing papers in PubMed.
- Evaluation of Facial Soft Tissue Angles in Adolescents with Angle Class I, II, and III Malocclusion Using Profile Image Analysis.Dentistry journal · 2026Article
- Comparative Analysis of Generative AI Language Models in Orthodontics: Evidence-Based Insights Into Perplexity, iASK, and ChatGPT 4o Mini.TheScientificWorldJournal · 2026Article
- Growth Prediction in Orthodontics: ASystematic Review of Past Methods up to Artificial Intelligence.Children (Basel, Switzerland) · 2025Review
- A Thorough Review of the Clinical Applications of Artificial Intelligence in Lung Cancer.Cancers · 2025Review
- Application of Artificial Intelligence in Orthodontics: Current State and Future Perspectives.Healthcare (Basel, Switzerland) · 2023Review
- Review
- Segmentation of Dynamic Total-Body [International journal of biomedical imaging · 2023Article
- Artificial intelligence in molecular imaging.Annals of translational medicine · 2021Review
- Artificial Intelligence for Response Evaluation With PET/CT.Seminars in nuclear medicine · 2021Review
- Joint Tumor Segmentation in PET-CT Images Using Co-Clustering and Fusion Based on Belief Functions.IEEE transactions on image processing : a publication of the IEEE Signal Processing Society · 2019Article
- Current Methods to Define Metabolic Tumor Volume in Positron Emission Tomography: Which One is Better?Nuclear medicine and molecular imaging · 2018Review
- Classification and evaluation strategies of auto-segmentation approaches for PET: Report of AAPM task group No. 211.Medical physics · 2017Article
- A review on segmentation of positron emission tomography images.Computers in biology and medicine · 2014Review
- Automatic detection and classification of nasopharyngeal carcinoma on PET/CT with support vector machine.International journal of computer assisted radiology and surgery · 2012Article
Corrections and comments
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Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Tumour detection, classification, and quantification in positron emission tomography (PET) imaging at early stage of disease are important issues for clinical diagnosis, assessment of response to treatment, and radiotherapy planning. Many techniques have been proposed for segmenting medical imaging data; however, some of the approaches have poor performance, large inaccuracy, and require substantial computation time for analysing large medical volumes. Artificial intelligence (AI) approaches can provide improved accuracy and save decent amount of time. Artificial neural networks (ANNs), as one of the best AI techniques, have the capability to classify and quantify precisely lesions and model the clinical evaluation for a specific problem. This paper presents a novel application of ANNs in the wavelet domain for PET volume segmentation. ANN performance evaluation using different training algorithms in both spatial and wavelet domains with a different number of neurons in the hidden layer is also presented. The best number of neurons in the hidden layer is determined according to the experimental results, which is also stated Levenberg-Marquardt backpropagation training algorithm as the best training approach for the proposed application. The proposed intelligent system results are compared with those obtained using conventional techniques including thresholding and clustering based approaches. Experimental and Monte Carlo simulated PET phantom data sets and clinical PET volumes of nonsmall cell lung cancer patients were utilised to validate the proposed algorithm which has demonstrated promising 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.