Search results for "prostate gland"

showing 5 items of 5 documents

Primary Mucinous Adenocarcinoma of the Urinary Bladder with Signet-Ring Cells: Description of an Uncommon Case and Critical Points in Its Management

2016

We present an uncommon case of mucinous adenocarcinoma of the bladder (MAB) with signet-ring cells extensively infiltrating prostate gland and pelvic/retroperitoneal lymph node stations and not responsive to usual systemic chemotherapy regimens. This case highlights the important features of MAB including the pattern of tumor spread, the tendency for initial misdiagnosis, and the importance of immunohistochemical study in order to define its primary origin from the bladder and choose the most appropriate treatment since the beginning.

Pathologymedicine.medical_specialtyUrinary bladderSignet ring cellSystemic chemotherapybusiness.industryRetroperitoneal Lymph Node030232 urology & nephrologyCase ReportGeneral Medicinemedicine.diseaselcsh:Diseases of the genitourinary system. Urologylcsh:RC870-92303 medical and health sciences0302 clinical medicinemedicine.anatomical_structure030220 oncology & carcinogenesismedicineAdenocarcinomaImmunohistochemistryProstate glandbusinessCase Reports in Urology
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Automatic classification of tissues on pelvic MRI based on relaxation times and support vector machine

2019

International audience; Tissue segmentation and classification in MRI is a challenging task due to a lack of signal intensity standardization. MRI signal is dependent on the acquisition protocol, the coil profile, the scanner type, etc. While we can compute quantitative physical tissue properties independent of the hardware and the sequence parameters, it is still difficult to leverage these physical properties to segment and classify pelvic tissues. The proposed method integrates quantitative MRI values (T1 and T2 relaxation times and pure synthetic weighted images) and machine learning (Support Vector Machine (SVM)) to segment and classify tissues in the pelvic region, i.e.: fat, muscle, …

MaleSupport Vector MachinePhysiologyComputer scienceBiochemistryDiagnostic Radiology030218 nuclear medicine & medical imagingFatsMachine Learning0302 clinical medicineBone MarrowProstateImmune PhysiologyRelaxation TimeMedicine and Health SciencesImage Processing Computer-AssistedSegmentationProspective StudiesMultidisciplinarymedicine.diagnostic_testPhysicsRadiology and ImagingQRelaxation (NMR)RMagnetic Resonance ImagingLipidsmedicine.anatomical_structurePhysical SciencesMedicineAnatomyResearch ArticleAdultComputer and Information SciencesImaging TechniquesScienceBladderImmunologyImage processingResearch and Analysis MethodsPelvis03 medical and health sciencesExocrine GlandsDiagnostic MedicineArtificial IntelligenceSupport Vector Machinesmedicine[INFO.INFO-IM]Computer Science [cs]/Medical ImagingHumansRelaxation (Physics)PelvisPelvic MRIbusiness.industryBiology and Life SciencesMagnetic resonance imagingPattern recognitionRenal SystemSupport vector machineImmune SystemSpin echoProstate GlandArtificial intelligenceBone marrowbusiness030217 neurology & neurosurgery
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Automated prostate gland segmentation based on an unsupervised fuzzy C-means clustering technique using multispectral T1w and T2w MR imaging

2017

Prostate imaging analysis is difficult in diagnosis, therapy, and staging of prostate cancer. In clinical practice, Magnetic Resonance Imaging (MRI) is increasingly used thanks to its morphologic and functional capabilities. However, manual detection and delineation of prostate gland on multispectral MRI data is currently a time-expensive and operator-dependent procedure. Efficient computer-assisted segmentation approaches are not yet able to address these issues, but rather have the potential to do so. In this paper, a novel automatic prostate MR image segmentation method based on the Fuzzy C-Means (FCM) clustering algorithm, which enables multispectral T1-weighted (T1w) and T2-weighted (T…

Computer scienceAutomated segmentation; Fuzzy C-Means clustering; Multispectral MR imaging; Prostate cancer; Prostate gland; Unsupervised machine learningMultispectral image02 engineering and technologyautomated segmentation; multispectral MR imaging; prostate gland; prostate cancer; unsupervised Machine Learning; Fuzzy C-Means clustering030218 nuclear medicine & medical imaging03 medical and health sciencesProstate cancer0302 clinical medicineProstate0202 electrical engineering electronic engineering information engineeringmedicineComputer visionSegmentationautomated segmentationunsupervised Machine LearningCluster analysisSettore ING-INF/05 - Sistemi Di Elaborazione Delle Informazionimedicine.diagnostic_testbusiness.industryINF/01 - INFORMATICAMagnetic resonance imagingmedicine.diseaseprostate cancerFuzzy C-Means clusteringmultispectral MR imagingmedicine.anatomical_structureUnsupervised learning020201 artificial intelligence & image processingArtificial intelligencebusinessprostate glandInformation SystemsMultispectral segmentation
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Zur Gef��-und Muskelarchitektur der menschlichen Prostata

1971

An 38 Drusen aus verschiedenen Lebensaltern wurde das Blutgefassystem und die Muskelarchitektur der Prostata untersucht. Die Blutgefase der Prostata bilden drei Provinzen: den Kapselplexus, den periurethralen Plexus und die Parenchymgefase. Die Arterien sind stark gewendelt und bilden nach der Pubertat entweder eine speziell gebaute Elastica externa oder elastische Intimapolster aus. Die Capillaren haben bei Kindern ein weiteres Lumen als beim Erwachsenen und liegen dichter beieinander. Die Venen des periurethralen Plexus zeigen pseudokavernose Anordnung, die des Kapselplexus sind durch auffallige Kaliberschwankungen gekennzeichnet. Die glatte Muskulatur der Prostata bildet ein zusammenhang…

GynecologyEmbryologymedicine.medical_specialtybusiness.industrymedicineCell BiologyAnatomyProstate glandbusinessDevelopmental BiologyZeitschrift f�r Anatomie und Entwicklungsgeschichte
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Fully automatic multispectral MR image segmentation of prostate gland based on the fuzzy C-means clustering algorithm

2017

Prostate imaging is a very critical issue in the clinical practice, especially for diagnosis, therapy, and staging of prostate cancer. Magnetic Resonance Imaging (MRI) can provide both morphologic and complementary functional information of tumor region. Manual detection and segmentation of prostate gland and carcinoma on multispectral MRI data is not easily practicable in the clinical routine because of the long times required by experienced radiologists to analyze several types of imaging data. In this paper, a fully automatic image segmentation method, exploiting an unsupervised Fuzzy C-Means (FCM) clustering technique for multispectral T1-weighted and T2-weighted MRI data processing, is…

Computer scienceMultispectral imageFully automatic segmentation; Multispectral MR imaging; Prostate cancer; Prostate gland; Unsupervised fuzzy C-means clusteringFuzzy logic030218 nuclear medicine & medical imaging03 medical and health sciencesProstate cancer0302 clinical medicineProstatemedicineSegmentationComputer visionCluster analysismedicine.diagnostic_testbusiness.industryINF/01 - INFORMATICAMagnetic resonance imagingfully automatic segmentationImage segmentationmedicine.diseaseprostate cancermultispectral MR imagingunsupervised Fuzzy C-Means clusteringmedicine.anatomical_structureArtificial intelligencebusinessprostate gland030217 neurology & neurosurgery
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