0000000000223752

AUTHOR

Francesco Marletta

showing 6 related works from this author

A fully automatic approach for multimodal PET and MR image segmentation in gamma knife treatment planning

2017

The aim of this study is to combine Biological Target Volume (BTV) segmentation and Gross Target Volume (GTV) segmentation in stereotactic neurosurgery.Our goal is to enhance Clinical Target Volume (CTV) definition, including metabolic and morphologic information, for treatment planning and patient follow-up.We propose a fully automatic approach for multimodal PET and MR image segmentation. This method is based on the Random Walker (RW) and Fuzzy C-Means clustering (FCM) algorithms. A total of 19 brain metastatic tumors, undergone stereotactic neuro-radiosurgery, were retrospectively analyzed. A framework for the evaluation of multimodal PET/MRI segmentation is presented, considering volume…

Radiotherapy PlanningBrain tumorHealth Informatics02 engineering and technologyFuzzy C-means clusteringRadiosurgeryBrain tumorsMultimodal ImagingING-INF/05 - SISTEMI DI ELABORAZIONE DELLE INFORMAZIONI030218 nuclear medicine & medical imaging03 medical and health sciencesComputer-Assisted0302 clinical medicineRandom walker algorithm0202 electrical engineering electronic engineering information engineeringHumansMedicineSegmentationComputer visionRadiation treatment planningCluster analysisImage resolutionPET/MR imagingModality (human–computer interaction)Brain Neoplasmsbusiness.industryRadiotherapy Planning Computer-AssistedINF/01 - INFORMATICAMultimodal therapymedicine.diseaseRandom Walker algorithmMagnetic Resonance ImagingComputer Science ApplicationsBrain tumorGamma knife treatmentPositron-Emission Tomography020201 artificial intelligence & image processingMultimodal image segmentationBrain tumors; Fuzzy C-means clustering; Gamma knife treatments; Multimodal image segmentation; PET/MR imaging; Random Walker algorithm; Brain Neoplasms; Humans; Radiosurgery; Magnetic Resonance Imaging; Multimodal Imaging; Positron-Emission Tomography; Radiotherapy Planning Computer-AssistedArtificial intelligencebusinessGamma knife treatmentsSoftware
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Biological target volume segmentation for radiotherapy treatment planning

2016

medicine.medical_specialtybusiness.industryBiophysicsGeneral Physics and Astronomy02 engineering and technologyGeneral MedicineRadiotherapy treatment planning021001 nanoscience & nanotechnologyBiological target0202 electrical engineering electronic engineering information engineeringMedicine020201 artificial intelligence & image processingRadiology Nuclear Medicine and imagingSegmentationRadiology0210 nano-technologybusinessVolume (compression)Physica Medica
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Clinical support in radiation therapy scenarios: MR brain tumor segmentation using an unsupervised fuzzy C-Means clustering technique

2016

medicine.medical_specialtyMR segmentationComputer sciencemedicine.medical_treatmentBiophysicsGeneral Physics and AstronomyFuzzy logicradiation therapy030218 nuclear medicine & medical imaging03 medical and health sciences0302 clinical medicineClinical supportmedicineRadiology Nuclear Medicine and imagingCluster analysisSemi-automatic segmentationNeuro-radiosurgery treatmentbusiness.industryPattern recognitionGeneral MedicineFuzzy C-Means clusteringRadiation therapy030220 oncology & carcinogenesisArtificial intelligenceRadiologybusinessBrain tumor segmentationbrain tumorMR imaging
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Gamma Knife treatment planning: MR brain tumor segmentation and volume measurement based on unsupervised Fuzzy C-Means clustering

2015

Nowadays, radiation treatment is beginning to intensively use MRI thanks to its greater ability to discriminate healthy and diseased soft-tissues. Leksell Gamma Knife® is a radio-surgical device, used to treat different brain lesions, which are often inaccessible for conventional surgery, such as benign or malignant tumors. Currently, the target to be treated with radiation therapy is contoured with slice-by-slice manual segmentation on MR datasets. This approach makes the segmentation procedure time consuming and operator-dependent. The repeatability of the tumor boundary delineation may be ensured only by using automatic or semiautomatic methods, supporting clinicians in the treatment pla…

Jaccard indexSimilarity (geometry)Computer scienceScale-space segmentationFuzzy logicunsupervised clusteringmagnetic resonance imagingSegmentationComputer visionmagnetic resonance imag- ingElectrical and Electronic EngineeringCluster analysisRadiation treatment planningSettore ING-INF/05 - Sistemi Di Elaborazione Delle InformazioniPixelbrain tumors; Gamma Knife treatment planning; magnetic resonance imaging; semi-automatic segmentation; unsupervised clusteringbusiness.industrybrain tumors Gamma Knife treatment planning magnetic resonance imaging semi-automatic segmentation unsupervised clusteringElectronic Optical and Magnetic Materialsbrain tumorsComputer Vision and Pattern RecognitionArtificial intelligencebusinesssemi-automatic segmentationSoftwarebrain tumorGamma Knife treatment planning
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Using anatomic and metabolic imaging in stereotactic radio neuro-surgery treatments

2016

PET/MR imagingmedicine.medical_specialtyNeuro-radiosurgerybusiness.industryMetabolic imagingBiophysicsGeneral Physics and AstronomyGeneral MedicineRandom Walker algorithmFuzzy C-Means clustering030218 nuclear medicine & medical imagingBrain tumor03 medical and health sciences0302 clinical medicineRandom walker algorithm030220 oncology & carcinogenesismedicineRadiology Nuclear Medicine and imagingNeurosurgeryRadiologyPet mr imagingbusinessNuclear medicine
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Semi-automatic Brain Lesion Segmentation in Gamma Knife Treatments Using an Unsupervised Fuzzy C-Means Clustering Technique

2016

MR Imaging is being increasingly used in radiation treatment planning as well as for staging and assessing tumor response. Leksell Gamma Knife (R) is a device for stereotactic neuro-radiosurgery to deal with inaccessible or insufficiently treated lesions with traditional surgery or radiotherapy. The target to be treated with radiation beams is currently contoured through slice-by-slice manual segmentation on MR images. This procedure is time consuming and operator-dependent. Segmentation result repeatability may be ensured only by using automatic/semi-automatic methods with the clinicians supporting the planning phase. In this paper a semi-automatic segmentation method, based on an unsuperv…

Computer scienceGamma knifeBrain lesions Gamma knife treatments MR imaging Semi-automatic segmentation Unsupervised FCM clusteringFuzzy logicBrain lesions; Gamma knife treatments; MR imaging; Semi-automatic segmentation; Unsupervised FCM clustering030218 nuclear medicine & medical imaging03 medical and health sciences0302 clinical medicineComputer visionSegmentationRadiation treatment planningCluster analysisSettore ING-INF/05 - Sistemi Di Elaborazione Delle InformazioniSemi-automatic segmentationBrain lesionsbusiness.industryMr imagingUnsupervised FCM clusteringBrain lesionGamma knife treatmentBrain lesionsSemi automaticArtificial intelligencebusinessGamma knife treatments030217 neurology & neurosurgeryMR imaging
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