0000000000543056

AUTHOR

Andrea Ranieri

showing 1 related works from this author

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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