Search results for "MRI segmentation"

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Towards a unified analysis of cerebellum maturation and aging across the entire lifespan: A MRI analysis

2021

[EN] Previous literature about the structural characterization of the human cerebellum is related to the context of a specific pathology or focused in a restricted age range. In fact, studies about the cerebellum maturation across the lifespan are scarce and most of them considered the cerebellum as a whole without investigating each lobule. This lack of study can be explained by the lack of both accurate segmentation methods and data availability. Fortunately, during the last years, several cerebellum segmenta- tion methods have been developed and many databases comprising subjects of dif- ferent ages have been made publically available. This fact opens an opportunity window to obtain a mo…

AdultMaleAgingcerebellum trajectoryAdolescentHuman DevelopmentPatch-based processing050105 experimental psychology03 medical and health sciencesYoung Adult0302 clinical medicineCerebellumMaturationImage Processing Computer-Assisted[INFO.INFO-IM]Computer Science [cs]/Medical ImagingHumans0501 psychology and cognitive sciencesRadiology Nuclear Medicine and imagingpatch-based processingGray MatterChildCerebellum trajectoryResearch ArticlesAgedMRI segmentationAged 80 and overLifespanRadiological and Ultrasound Technologymaturation05 social sciencesagingpatch‐based processingInfantMiddle AgedMagnetic Resonance ImagingWhite Matter3. Good healthNeurologyFISICA APLICADAChild PreschoolFemale[SDV.NEU]Life Sciences [q-bio]/Neurons and Cognition [q-bio.NC]Neurology (clinical)Anatomy030217 neurology & neurosurgerylifespanResearch Article
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Leveraging Uncertainty Estimates to Improve Segmentation Performance in Cardiac MR

2021

International audience; In medical image segmentation, several studies have used Bayesian neural networks to segment and quantify the uncertainty of the images. These studies show that there might be an increased epistemic uncertainty in areas where there are semantically and visually challenging pixels. The uncertain areas of the image can be of a great interest as they can possibly indicate the regions of incorrect segmentation. To leverage the uncertainty information, we propose a segmentation model that incorporates the uncertainty into its learning process. Firstly, we generate the uncertainty estimate (sample variance) using Monte-Carlo dropout during training. Then we incorporate it …

[INFO.INFO-AI] Computer Science [cs]/Artificial Intelligence [cs.AI]Bayesian deep learningCardiac MRI Segmentation[INFO.INFO-IM] Computer Science [cs]/Medical ImagingComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISIONUncertainty[INFO.INFO-IM]Computer Science [cs]/Medical ImagingMyocardial scar[INFO.INFO-AI]Computer Science [cs]/Artificial Intelligence [cs.AI]
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