ArticlePloS one2018
Automatic lesion detection and segmentation of 18F-FET PET in gliomas: A full 3D U-Net convolutional neural network study.
Article in PloS one, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 62 papers.
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Who cites it
62 citing papers in PubMed, 156 citations in OpenAlex.
- Artificial intelligence in medicine: Current applications in cardiology, oncology, and radiology.World journal of methodology · 2025Review
- Lymph node disease in 2-deoxy-2-fluorodeoxyglucose positron emission tomography/computed tomography imaging: Advances in artificial intelligence-driven automatic segmentation and precise diagnosis.World journal of clinical oncology · 2025Review
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- Systematic double reading for oncological PET/CT scans: insights from a prospective multicentre study in 678 patients.EJNMMI reports · 2025Article
- Interobserver ground-truth variability limits performance of automated glioblastoma segmentation on [EJNMMI physics · 2025Article
- Characterization of artificial intelligence performance for lesion detection using synthetic lesions in PET imaging.Medical physics · 2025Article
- A privacy-preserved horizontal federated learning for malignant glioma tumour detection using distributed data-silos.PloS one · 2025Article
- Brain CT image classification based on mask RCNN and attention mechanism.Scientific reports · 2024Article
- Semi-supervised learning towards automated segmentation of PET images with limited annotations: application to lymphoma patients.Physical and engineering sciences in medicine · 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
- Detection and Segmentation of Radiolucent Lesions in the Lower Jaw on Panoramic Radiographs Using Deep Neural Networks.Medicina (Kaunas, Lithuania) · 2023Article
- Delineation and agreement of FET PET biological volumes in glioblastoma: results of the nuclear medicine credentialing program from the prospective, multi-centre trial evaluating FET PET In Glioblastoma (FIG) study-TROG 18.06.European journal of nuclear medicine and molecular imaging · 2023Article
- A multi-label CNN model for the automatic detection and segmentation of gliomas using [European journal of nuclear medicine and molecular imaging · 2023Article
- Head and neck tumor segmentation convolutional neural network robust to missing PET/CT modalities using channel dropout.Physics in medicine and biology · 2023Article
- Automatic segmentation of prostate and extracapsular structures in MRI to predict needle deflection in percutaneous prostate intervention.International journal of computer assisted radiology and surgery · 2023Article
- Need for objective task-based evaluation of AI-based segmentation methods for quantitative PET.Proceedings of SPIE--the International Society for Optical Engineering · 2023Article
- Automatic segmentation and measurement of pressure injuries using deep learning models and a LiDAR camera.Scientific reports · 2023Article
- From coarse to fine: a deep 3D probability volume contours framework for tumour segmentation and dose painting in PET images.Frontiers in radiology · 2023Article
- Artificial Intelligence and Precision Medicine: A New Frontier for the Treatment of Brain Tumors.Life (Basel, Switzerland) · 2022Review
- Automatic lesion detection and segmentation inBiomedical engineering online · 2022Article
2 more citing papers are in PubMed but not listed here.
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Authors and funding
5 authors at 2 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
introductionAmino-acids positron emission tomography (PET) is increasingly used in the diagnostic workup of patients with gliomas, including differential diagnosis, evaluation of tumor extension, treatment planning and follow-up. Recently, progresses of computer vision and machine learning have been translated for medical imaging. Aim was to demonstrate the feasibility of an automated 18F-fluoro-ethyl-tyrosine (18F-FET) PET lesion detection and segmentation relying on a full 3D U-Net Convolutional Neural Network (CNN).
methodsAll dynamic 18F-FET PET brain image volumes were temporally realigned to the first dynamic acquisition, coregistered and spatially normalized onto the Montreal Neurological Institute template. Ground truth segmentations were obtained using manual delineation and thresholding (1.3 x background). The volumetric CNN was implemented based on a modified Keras implementation of a U-Net library with 3 layers for the encoding and decoding paths. Dice similarity coefficient (DSC) was used as an accuracy measure of segmentation.
resultsThirty-seven patients were included (26 [70%] in the training set and 11 [30%] in the validation set). All 11 lesions were accurately detected with no false positive, resulting in a sensitivity and a specificity for the detection at the tumor level of 100%. After 150 epochs, DSC reached 0.7924 in the training set and 0.7911 in the validation set. After morphological dilatation and fixed thresholding of the predicted U-Net mask a substantial improvement of the DSC to 0.8231 (+ 4.1%) was noted. At the voxel level, this segmentation led to a 0.88 sensitivity [95% CI, 87.1 to, 88.2%] a 0.99 specificity [99.9 to 99.9%], a 0.78 positive predictive value: [76.9 to 78.3%], and a 0.99 negative predictive value [99.9 to 99.9%].
conclusionsWith relatively high performance, it was proposed the first full 3D automated procedure for segmentation of 18F-FET PET brain images of patients with different gliomas using a U-Net CNN architecture.
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