0000000000866795

AUTHOR

Ramona Woitek

showing 2 related works from this author

Semi-automated and interactive segmentation of contrast-enhancing masses on breast DCE-MRI using spatial fuzzy clustering

2022

Abstract Multiparametric Magnetic Resonance Imaging (MRI) is the most sensitive imaging modality for breast cancer detection and is increasingly playing a key role in lesion characterization. In this context, accurate and reliable quantification of the shape and extent of breast cancer is crucial in clinical research environments. Since conventional lesion delineation procedures are still mostly manual, automated segmentation approaches can improve this time-consuming and operator-dependent task by annotating the regions of interest in a reproducible manner. In this work, a semi-automated and interactive approach based on the spatial Fuzzy C-Means (sFCM) algorithm is proposed, used to segme…

Fuzzy clusteringUnsupervised fuzzy clusteringbusiness.industryComputer scienceBiomedical EngineeringHealth InformaticsPattern recognitionImage processingContext (language use)Image segmentationComputer-assisted lesion detectionMagnetic Resonance ImagingThresholdingConvolutional neural networkBreast cancer; Computer-assisted lesion detection; Magnetic Resonance Imaging; Semi-automated segmentation; Spatial information; Unsupervised fuzzy clusteringBreast cancerSignal ProcessingSemi-automated segmentationSpatial informationSegmentationArtificial intelligencebusinessMultiparametric Magnetic Resonance ImagingBiomedical Signal Processing and Control
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3D DCE-MRI Radiomic Analysis for Malignant Lesion Prediction in Breast Cancer Patients

2022

Rationale and Objectives: To develop and validate a radiomic model, with radiomic features extracted from breast Dynamic Contrast-Enhanced Magnetic Resonance Imaging (DCE-MRI) from a 1.5T scanner, for predicting the malignancy of masses with enhancement. Images were acquired using an 8-channel breast coil in the axial plane. The rationale behind this study is to show the feasibility of a radio-mics-powered model that could be integrated into the clinical practice by exploiting only standard-of-care DCE-MRI with the goal of reducing the required image pre-processing (ie, normalization and quantitative imaging map generation).Materials and Methods: 107 radiomic features were extracted from a …

Breast cancer Dynamic contrast-enhanced magnetic resonance imagingSupport Vector MachineComputer scienceNormalization (image processing)Breast NeoplasmsFeature selectionBreast cancerBreast cancerDiscriminative modelmedicineHumansRadiology Nuclear Medicine and imagingBreastRetrospective StudiesDynamic contrast-enhanced magnetic resonance imagingRadiomicsSupport vector machinesReceiver operating characteristicbusiness.industryPattern recognitionmedicine.diseaseMagnetic Resonance Imagingmachine learning Radiomics unsupervised feature selection Support vector machinesSupport vector machinemachine learningROC CurveFeature (computer vision)Test setFemaleArtificial intelligenceSettore MED/36 - Diagnostica Per Immagini E Radioterapiabusinessunsupervised feature selectionBreast cancer Dynamic contrast-enhanced magnetic resonance imaging; machine learning Radiomics unsupervised feature selection Support vector machinesAcademic Radiology
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