Search results for "MR Prostate"

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Robust Resolution-Enhanced Prostate Segmentation in Magnetic Resonance and Ultrasound Images through Convolutional Neural Networks

2021

[EN] Prostate segmentations are required for an ever-increasing number of medical applications, such as image-based lesion detection, fusion-guided biopsy and focal therapies. However, obtaining accurate segmentations is laborious, requires expertise and, even then, the inter-observer variability remains high. In this paper, a robust, accurate and generalizable model for Magnetic Resonance (MR) and three-dimensional (3D) Ultrasound (US) prostate image segmentation is proposed. It uses a densenet-resnet-based Convolutional Neural Network (CNN) combined with techniques such as deep supervision, checkpoint ensembling and Neural Resolution Enhancement. The MR prostate segmentation model was tra…

Computer scienceMR prostate imagingUS prostate imagingINGENIERIA MECANICAconvolutional neural networklcsh:TechnologyConvolutional neural network030218 nuclear medicine & medical imaginglcsh:Chemistry03 medical and health sciences0302 clinical medicinemedicineGeneral Materials Sciencelcsh:QH301-705.5Instrumentation030304 developmental biologyFluid Flow and Transfer Processes0303 health sciencesmedicine.diagnostic_testlcsh:Tbusiness.industryProcess Chemistry and TechnologyConvolutional Neural NetworksUltrasoundResolution (electron density)General EngineeringMagnetic resonance imagingPattern recognitionProstate Segmentationlcsh:QC1-999Computer Science ApplicationsNeural resolution enhancementlcsh:Biology (General)lcsh:QD1-999lcsh:TA1-2040Christian ministryArtificial intelligencelcsh:Engineering (General). Civil engineering (General)Magnetic Resonance and Ultrasound Imagesbusinesslcsh:PhysicsProstate segmentationApplied Sciences
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Joint Probability of Shape and Image Similarities to Retrieve 2D TRUS-MR Slice Correspondence for Prostate Biopsy

2012

International audience; This paper presents a novel method to identify the 2D axial Magnetic Resonance (MR) slice from a pre-acquired MR prostate volume that closely corresponds to the 2D axial Transrectal Ultrasound (TRUS) slice obtained during prostate biopsy. The method combines both shape and image intensity information. The segmented prostate contours in both the imaging modalities are described by shape-context representations and matched using the Chi-square distance. Normalized mutual information and correlation coefficient between the TRUS and MR slices are computed to find image similarities. Finally, the joint probability values comprising shape and image similarities are used in…

MaleProstate biopsyBiopsy[INFO.INFO-IM] Computer Science [cs]/Medical Imaging030230 surgeryNormalized mutual information030218 nuclear medicine & medical imagingImage (mathematics)03 medical and health sciences0302 clinical medicineJoint probability distribution[INFO.INFO-IM]Computer Science [cs]/Medical ImagingMedicineHumansComputer visionMR ProstateProbabilitymedicine.diagnostic_test[ INFO.INFO-IM ] Computer Science [cs]/Medical Imagingbusiness.industryUltrasoundProstatic NeoplasmsMagnetic resonance imagingImage segmentationMagnetic Resonance ImagingArtificial intelligencebusiness
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