ArticleInternational journal of molecular imaging2013
Automatic Segmentation of Lung Carcinoma Using 3D Texture Features in 18-FDG PET/CT.
Article in International journal of molecular imaging, 2013. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 papers.
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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
17 citing papers in PubMed.
- Review
- Article
- Potential advantages of FDG-PET radiomic feature map for target volume delineation in lung cancer radiotherapy.Journal of applied clinical medical physics · 2022Article
- Novel Harmonization Method for Multi-Centric Radiomic Studies in Non-Small Cell Lung Cancer.Current oncology (Toronto, Ont.) · 2022Article
- A Novel Radiomics-Based Tumor Volume Segmentation Algorithm for Lung Tumors in FDG-PET/CT after 3D Motion Correction-A Technical Feasibility and Stability Study.Diagnostics (Basel, Switzerland) · 2022Article
- Artificial Intelligence for Response Evaluation With PET/CT.Seminars in nuclear medicine · 2021Review
- A Systematic Review of PET Textural Analysis and Radiomics in Cancer.Diagnostics (Basel, Switzerland) · 2021Review
- Repeatability of two semi-automatic artificial intelligence approaches for tumor segmentation in PET.EJNMMI research · 2021Article
- Deep Learning for Variational Multimodality Tumor Segmentation in PET/CT.Neurocomputing · 2020Article
- Conventional Filtering Versus U-Net Based Models for Pulmonary Nodule Segmentation in CT Images.Journal of medical systems · 2020Article
- Variational PET/CT Tumor Co-segmentation Integrated with PET Restoration.IEEE transactions on radiation and plasma medical sciences · 2020Article
- Use of Crowd Innovation to Develop an Artificial Intelligence-Based Solution for Radiation Therapy Targeting.JAMA oncology · 2019Article
- Dual-modality multi-atlas segmentation of torso organs from [International journal of computer assisted radiology and surgery · 2019Article
- 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
- Classification and evaluation strategies of auto-segmentation approaches for PET: Report of AAPM task group No. 211.Medical physics · 2017Article
- Computer-assisted delineation of lung tumor regions in treatment planning CT images with PET/CT image sets based on an optimum contour selection method.Journal of radiation research · 2014Article
- A review on segmentation of positron emission tomography images.Computers in biology and medicine · 2014Review
Corrections and comments
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Authors and funding
7 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Target definition is the largest source of geometric uncertainty in radiation therapy. This is partly due to a lack of contrast between tumor and healthy soft tissue for computed tomography (CT) and due to blurriness, lower spatial resolution, and lack of a truly quantitative unit for positron emission tomography (PET). First-, second-, and higher-order statistics, Tamura, and structural features were characterized for PET and CT images of lung carcinoma and organs of the thorax. A combined decision tree (DT) with K-nearest neighbours (KNN) classifiers as nodes containing combinations of 3 features were trained and used for segmentation of the gross tumor volume. This approach was validated for 31 patients from two separate institutions and scanners. The results were compared with thresholding approaches, the fuzzy clustering method, the 3-level fuzzy locally adaptive Bayesian algorithm, the multivalued level set algorithm, and a single KNN using Hounsfield units and standard uptake value. The results showed the DTKNN classifier had the highest sensitivity of 73.9%, second highest average Dice coefficient of 0.607, and a specificity of 99.2% for classifying voxels when using a probabilistic ground truth provided by simultaneous truth and performance level estimation using contours drawn by 3 trained physicians.
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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.