Search results for "biological target volume"

showing 7 items of 7 documents

K-nearest neighbor driving active contours to delineate biological tumor volumes

2019

Abstract An algorithm for tumor delineation in positron emission tomography (PET) is presented. Segmentation is achieved by a local active contour algorithm, integrated and optimized with the k-nearest neighbor (KNN) classification method, which takes advantage of the stratified k-fold cross-validation strategy. The proposed approach is evaluated considering the delineation of cancers located in different body districts (i.e. brain, head and neck, and lung), and considering different PET radioactive tracers. Data are pre-processed in order to be expressed in terms of standardized uptake value, the most widely used PET quantification index. The algorithm uses an initial, operator selected re…

0209 industrial biotechnologyK-nearest neighborComputer scienceComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISIONFDG and MET PET imagingStandardized uptake value02 engineering and technologyImaging phantomk-nearest neighbors algorithmActive contour algorithm020901 industrial engineering & automationArtificial IntelligenceRegion of interest0202 electrical engineering electronic engineering information engineeringSegmentationElectrical and Electronic EngineeringActive contour modelbusiness.industryProcess (computing)Pattern recognitionCancer segmentationBiological target volumeControl and Systems Engineering020201 artificial intelligence & image processingArtificial intelligencebusinessEnergy (signal processing)Engineering Applications of Artificial Intelligence
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A smart and operator independent system to delineate tumours in Positron Emission Tomography scans

2018

Abstract Positron Emission Tomography (PET) imaging has an enormous potential to improve radiation therapy treatment planning offering complementary functional information with respect to other anatomical imaging approaches. The aim of this study is to develop an operator independent, reliable, and clinically feasible system for biological tumour volume delineation from PET images. Under this design hypothesis, we combine several known approaches in an original way to deploy a system with a high level of automation. The proposed system automatically identifies the optimal region of interest around the tumour and performs a slice-by-slice marching local active contour segmentation. It automa…

Lung NeoplasmsComputer sciencemedicine.medical_treatmentPET imagingPattern Recognition Automated030218 nuclear medicine & medical imaging0302 clinical medicineNeoplasmsImage Processing Computer-AssistedSegmentationDiagnosis Computer-AssistedNeoplasm MetastasisRadiation treatment planningSettore ING-INF/05 - Sistemi Di Elaborazione Delle InformazioniObserver VariationActive contour modelmedicine.diagnostic_testBrain NeoplasmsPhantoms ImagingComputer Science ApplicationsHead and Neck NeoplasmsPositron emission tomography030220 oncology & carcinogenesis18F-fluoro-2-deoxy-d-glucoseAlgorithms18F-fluoro-2-deoxy-d-glucose and 11C-labeled methionine PET imagingSimilarity (geometry)Health InformaticsSensitivity and SpecificityNOActive contour algorithm03 medical and health sciencesFluorodeoxyglucose F18Predictive Value of TestsRegion of interestmedicineHumansFalse Positive ReactionsRetrospective Studies18F-fluoro-2-deoxy-d-glucose 11C-labeled methionine PET imaging Active contour algorithm Biological target volume Cancer segmentationbusiness.industryRadiotherapy Planning Computer-Assisted11C-labeled methionineReproducibility of ResultsPattern recognitionGold standard (test)Cancer segmentationRadiation therapyBiological target volumePositron-Emission TomographyArtificial intelligenceTomography X-Ray ComputedbusinessSoftwareComputers in Biology and Medicine
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A preliminary PET radiomics study of brain metastases using a fully automatic segmentation method

2020

AbstractBackgroundPositron Emission Tomography (PET) is increasingly utilized in radiomics studies for treatment evaluation purposes. Nevertheless, lesion volume identification in PET images is a critical and still challenging step in the process of radiomics, due to the low spatial resolution and high noise level of PET images. Currently, the biological target volume (BTV) is manually contoured by nuclear physicians, with a time expensive and operator-dependent procedure.This study aims to obtain BTVs from cerebral metastases in patients who underwent L-[11C]methionine (11C-MET) PET, using a fully automatic procedure and to use these BTVs to extract radiomics features to stratify between p…

MalePositron emission tomographyComputer scienceLesion volumelcsh:Computer applications to medicine. Medical informaticsBiochemistry030218 nuclear medicine & medical imagingLesion03 medical and health sciences0302 clinical medicineRadiomicsStructural BiologyArtificial IntelligencemedicineHumansSegmentationNeoplasm Metastasislcsh:QH301-705.5Molecular BiologyCancerActive contour modelRadiomicsmedicine.diagnostic_testBrain Neoplasmsbusiness.industryApplied MathematicsResearchCancerPattern recognitionMiddle AgedPrognosismedicine.diseaseComputer Science ApplicationsCancer treatmentBiological target volumelcsh:Biology (General)Positron emission tomographyFeature (computer vision)030220 oncology & carcinogenesisPositron-Emission TomographyFully automaticlcsh:R858-859.7FemaleActive contourArtificial intelligencemedicine.symptomRadiomicActive contour; Biological target volume; Cancer; Positron emission tomography; Radiomics.businessBMC Bioinformatics
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Active contour algorithm with discriminant analysis for delineating tumors in positron emission tomography.

2019

Abstract In the context of cancer delineation using positron emission tomography datasets, we present an innovative approach which purpose is to tackle the real-time, three-dimensional segmentation task in a full, or at least nearly full automatized way. The approach comprises a preliminary initialization phase where the user highlights a region of interest around the cancer on just one slice of the tomographic dataset. The algorithm takes care of identifying an optimal and user-independent region of interest around the anomalous tissue and located on the slice containing the highest standardized uptake value so to start the successive segmentation task. The three-dimensional volume is then…

Positron emission tomographyComputer scienceInitializationMedicine (miscellaneous)Context (language use)Imaging phantomActive contour algorithm03 medical and health sciences0302 clinical medicineRegion of interestArtificial IntelligenceNeoplasmsmedicineHumansSegmentation030304 developmental biologyRetrospective Studies0303 health sciencesActive contour modelDiscriminant analysimedicine.diagnostic_testbusiness.industryDiscriminant AnalysisPattern recognitionLinear discriminant analysisPositron emission tomographyBiological target volume segmentationPositron-Emission TomographyArtificial intelligencebusiness030217 neurology & neurosurgeryAlgorithmsArtificial intelligence in medicine
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An enhanced random walk algorithm for delineation of head and neck cancers in PET studies

2017

An algorithm for delineating complex head and neck cancers in positron emission tomography (PET) images is presented in this article. An enhanced random walk (RW) algorithm with automatic seed detection is proposed and used to make the segmentation process feasible in the event of inhomogeneous lesions with bifurcations. In addition, an adaptive probability threshold and a k-means based clustering technique have been integrated in the proposed enhanced RW algorithm. The new threshold is capable of following the intensity changes between adjacent slices along the whole cancer volume, leading to an operator-independent algorithm. Validation experiments were first conducted on phantom studies:…

Similarity (geometry)Computer sciencePET imagingBiomedical EngineeringRandom walk030218 nuclear medicine & medical imaging03 medical and health sciences0302 clinical medicinemedicineImage Processing Computer-AssistedHumansSegmentationComputer visionCluster analysisEvent (probability theory)Settore ING-INF/05 - Sistemi Di Elaborazione Delle Informazionimedicine.diagnostic_testbusiness.industryPhantoms ImagingBiological target volume; Head and neck cancer segmentation; PET imaging; Random walksComputer Science ApplicationPattern recognitionRandom walkComputer Science ApplicationsBiological target volumeHausdorff distancePositron emission tomographyHead and Neck Neoplasms030220 oncology & carcinogenesisPositron-Emission TomographyArtificial intelligenceHead and neck cancer segmentationComputer Vision and Pattern RecognitionbusinessAlgorithmsBiological target volume Head and neck cancer segmentation PET imaging Random walks Algorithms Head and Neck Neoplasms Humans Image Processing Computer-Assisted Phantoms Imaging Positron-Emission TomographyVolume (compression)
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A fully automatic method for biological target volume segmentation of brain metastases

2016

Leksell Gamma Knife is a mini-invasive technique to obtain a complete destruction of cerebral lesions delivering a single high dose radiation beam. Positron Emission Tomography (PET) imaging is increasingly utilized for radiation treatment planning. Nevertheless, lesion volume delineation in PET datasets is challenging because of the low spatial resolution and high noise level of PET images. Nowadays, the biological target volume (BTV) is manually contoured on PET studies. This procedure is time expensive and operator-dependent. In this article, a fully automatic algorithm for the BTV delineation based on random walks (RW) on graphs is proposed. The results are compared with the outcomes of…

gamma knifePET imagingcerebral tumors segmentation030218 nuclear medicine & medical imagingrandom walk03 medical and health sciences0302 clinical medicinemedicineSegmentationElectrical and Electronic EngineeringRadiation treatment planningCluster analysisImage resolution1707Settore ING-INF/05 - Sistemi Di Elaborazione Delle Informazionimedicine.diagnostic_testbusiness.industryElectronic Optical and Magnetic Materialbiological target volumePattern recognitionThresholdingElectronic Optical and Magnetic MaterialsRegion growingPositron emission tomography030220 oncology & carcinogenesisbiological target volume cerebral tumors segmentation gamma knife PET imaging random walkComputer Vision and Pattern RecognitionArtificial intelligenceNuclear medicinebusinessSoftwareVolume (compression)International Journal of Imaging Systems and Technology
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An automatic method for metabolic evaluation of gamma knife treatments

2015

Lesion volume delineation of Positron Emission Tomography images is challenging because of the low spatial resolution and high noise level. Aim of this work is the development of an operator independent segmentation method of metabolic images. For this purpose, an algorithm for the biological tumor volume delineation based on random walks on graphs has been used. Twenty-four cerebral tumors are segmented to evaluate the functional follow-up after Gamma Knife radiotherapy treatment. Experimental results show that the segmentation algorithm is accurate and has real-time performance. In addition, it can reflect metabolic changes useful to evaluate radiotherapy response in treated patients.

medicine.diagnostic_testComputer sciencebusiness.industrymedicine.medical_treatmentComputer Science (all)PET imagingPattern recognitionLesion volumeRandom walkGamma knifeTheoretical Computer ScienceRadiation therapyBiological target volumeSegmentationBiological target volume Gamma Knife treatment PET imaging Random walk SegmentationPositron emission tomographymedicineSegmentationRadiotherapy treatmentGamma Knife treatmentArtificial intelligenceNoise levelbusinessImage resolution
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