Search results for "computer-assisted segmentation"

showing 2 items of 2 documents

GTVcut for neuro-radiosurgery treatment planning: an MRI brain cancer seeded image segmentation method based on a cellular automata model

2018

Despite of the development of advanced segmentation techniques, achieving accurate and reproducible gross tumor volume (GTV) segmentation results is still an important challenge in neuro-radiosurgery. Nowadays, magnetic resonance imaging (MRI) is the most prominent modality in radiation therapy for soft-tissue anatomical districts. Gamma Knife stereotactic neuro-radiosurgery is a minimally invasive technology for dealing with inaccessible or insufficiently treated tumors with traditional surgery or radiotherapy. During a treatment planning phase, the GTV is generally contoured by experienced neurosurgeons and radiation oncologists using fully manual segmentation procedures on MR images. Unf…

Cellular automataBrain cancersING-INF/06 - BIOINGEGNERIA ELETTRONICA E INFORMATICABrain cancers; Cellular automata; Computer-assisted segmentation; Gamma Knife neuro-radiosurgery; MR imagingComputer sciencemedicine.medical_treatment02 engineering and technologyBrain cancerRadiosurgeryING-INF/05 - SISTEMI DI ELABORAZIONE DELLE INFORMAZIONI030218 nuclear medicine & medical imaging03 medical and health sciences0302 clinical medicine0202 electrical engineering electronic engineering information engineeringmedicineSegmentationRadiation treatment planningModality (human–computer interaction)medicine.diagnostic_testbusiness.industryComputer Science ApplicationComputer-assisted segmentationINF/01 - INFORMATICAMagnetic resonance imagingPattern recognitionGamma Knife neuro-radiosurgeryComputer Science Applications1707 Computer Vision and Pattern RecognitionImage segmentationCellular automatonComputer Science ApplicationsRadiation therapy020201 artificial intelligence & image processingComputer Vision and Pattern RecognitionArtificial intelligencebusinessMR imaging
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On Unsupervised Methods for Medical Image Segmentation: Investigating Classic Approaches in Breast Cancer DCE-MRI

2021

Unsupervised segmentation techniques, which do not require labeled data for training and can be more easily integrated into the clinical routine, represent a valid solution especially from a clinical feasibility perspective. Indeed, large-scale annotated datasets are not always available, undermining their immediate implementation and use in the clinic. Breast cancer is the most common cause of cancer death in women worldwide. In this study, breast lesion delineation in Dynamic Contrast Enhanced MRI (DCE-MRI) series was addressed by means of four popular unsupervised segmentation approaches: Split-and-Merge combined with Region Growing (SMRG), k-means, Fuzzy C-Means (FCM), and spatial FCM (…

Fluid Flow and Transfer ProcessesTechnologymedical image segmentationQH301-705.5Process Chemistry and TechnologyTPhysicsQC1-999pattern recognitionGeneral EngineeringEngineering (General). Civil engineering (General)Breast cancer; Clinical feasibility; Computer-assisted segmentation; Machine learning; Magnetic resonance imaging; Medical image segmentation; Pattern recognitionComputer Science ApplicationsChemistrybreast cancermachine learningclinical feasibilitymagnetic resonance imagingGeneral Materials Sciencemedical image segmentation; breast cancer; pattern recognition; machine learning; clinical feasibility; magnetic resonance imaging; computer-assisted segmentationTA1-2040Biology (General)InstrumentationQD1-999computer-assisted segmentation
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