Search results for "Anatomical MRI"

showing 3 items of 3 documents

Neuroanatomical substrate of noise sensitivity.

2018

Recent functional studies suggest that noise sensitivity, a trait describing attitudes towards noise and predicting noise annoyance, is associated with altered processing in the central auditory system. In the present work, we examined whether noise sensitivity could be related to the structural anatomy of auditory and limbic brain areas. Anatomical MR brain images of 80 subjects were parcellated with FreeSurfer to measure grey matter volume, cortical thickness, cortical area and folding index of anatomical structures in the temporal lobe and insular cortex. The grey matter volume of amygdala and hippocampus was measured as well. According to our findings, noise sensitivity is associated wi…

0301 basic medicineAuditory perceptionAdultMaleyliherkkyysCognitive NeurosciencePlanum temporaleright anterior insulaGrey matterAuditory cortexInsular cortexta3112HippocampusTemporal lobe03 medical and health sciencesYoung Adult0302 clinical medicinemedicineAuditory systemauditory cortexHumansmagneettitutkimushippokampusGray MatterAuditory CortexCerebral Cortexnoise sensitivityMiddle AgedAmygdalakuuloMagnetic Resonance Imagingmeluanatomical MRINoise030104 developmental biologymedicine.anatomical_structureNeurologyAuditory PerceptionFemalePsychologyNoiseNeuroscience030217 neurology & neurosurgeryPersonalityNeuroImage
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USE-Net: Incorporating Squeeze-and-Excitation blocks into U-Net for prostate zonal segmentation of multi-institutional MRI datasets

2019

Prostate cancer is the most common malignant tumors in men but prostate Magnetic Resonance Imaging (MRI) analysis remains challenging. Besides whole prostate gland segmentation, the capability to differentiate between the blurry boundary of the Central Gland (CG) and Peripheral Zone (PZ) can lead to differential diagnosis, since tumor's frequency and severity differ in these regions. To tackle the prostate zonal segmentation task, we propose a novel Convolutional Neural Network (CNN), called USE-Net, which incorporates Squeeze-and-Excitation (SE) blocks into U-Net. Especially, the SE blocks are added after every Encoder (Enc USE-Net) or Encoder-Decoder block (Enc-Dec USE-Net). This study ev…

FOS: Computer and information sciences0209 industrial biotechnologyComputer Science - Machine LearningGeneralizationComputer scienceComputer Vision and Pattern Recognition (cs.CV)Cognitive NeuroscienceComputer Science - Computer Vision and Pattern RecognitionConvolutional neural network02 engineering and technologyConvolutional neural networkMachine Learning (cs.LG)Image (mathematics)Prostate cancer020901 industrial engineering & automationArtificial IntelligenceProstate0202 electrical engineering electronic engineering information engineeringmedicineMedical imagingAnatomical MRISegmentationBlock (data storage)Prostate cancermedicine.diagnostic_testSettore INF/01 - Informaticabusiness.industryAnatomical MRI; Convolutional neural networks; Cross-dataset generalization; Prostate cancer; Prostate zonal segmentation; USE-NetINF/01 - INFORMATICAMagnetic resonance imagingPattern recognitionUSE-Netmedicine.diseaseComputer Science Applicationsmedicine.anatomical_structureCross-dataset generalizationFeature (computer vision)Prostate zonal segmentation020201 artificial intelligence & image processingConvolutional neural networksArtificial intelligencebusinessEncoder
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CNN-Based Prostate Zonal Segmentation on T2-Weighted MR Images: A Cross-Dataset Study

2020

Prostate cancer is the most common cancer among US men. However, prostate imaging is still challenging despite the advances in multi-parametric magnetic resonance imaging (MRI), which provides both morphologic and functional information pertaining to the pathological regions. Along with whole prostate gland segmentation, distinguishing between the central gland (CG) and peripheral zone (PZ) can guide toward differential diagnosis, since the frequency and severity of tumors differ in these regions; however, their boundary is often weak and fuzzy. This work presents a preliminary study on deep learning to automatically delineate the CG and PZ, aiming at evaluating the generalization ability o…

Urologic DiseasesComputer scienceContext (language use)32 Biomedical and Clinical Sciences-Convolutional neural networkDeep convolutional neural networks Prostate zonal segmentation Cross-dataset generalizationProstate cancer46 Information and Computing SciencesProstateDeep convolutional neural networksmedicineAnatomical MRISegmentationProstate zonal segmentation; Prostate cancer; Anatomical MRI; Deep convolutional neural networks; Cross-dataset generalization;3202 Clinical SciencesCancerSettore ING-INF/05 - Sistemi Di Elaborazione Delle InformazioniProstate cancerSettore INF/01 - Informaticamedicine.diagnostic_testbusiness.industryDeep learningINF/01 - INFORMATICAMagnetic resonance imagingPattern recognitionmedicine.disease3211 Oncology and Carcinogenesismedicine.anatomical_structureCross-dataset generalizationProstate zonal segmentationBiomedical ImagingArtificial intelligenceDeep convolutional neural networkbusinessT2 weightedAnatomical MRI; Cross-dataset generalization; Deep convolutional neural networks; Prostate cancer; Prostate zonal segmentation
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