A5052077725- Cerebrovascular and Carotid Artery Diseases
- Cardiovascular Health and Disease Prevention
- Radiomics and Machine Learning in Medical Imaging
- Medical Image Segmentation Techniques
- AI in cancer detection
- Coronary Interventions and Diagnostics
- Cardiac Imaging and Diagnostics
- Photoacoustic and Ultrasonic Imaging
- Brain Tumor Detection and Classification
- Cardiovascular Function and Risk Factors
- Medical Imaging Techniques and Applications
- Cardiac Valve Diseases and Treatments
- Glioma Diagnosis and Treatment
- Digital Imaging for Blood Diseases
- COVID-19 diagnosis using AI
- Ultrasound Imaging and Elastography
- Advanced Neural Network Applications
- Lung Cancer Diagnosis and Treatment
- Ultrasound and Hyperthermia Applications
- Thyroid Cancer Diagnosis and Treatment
- Peripheral Nerve Disorders
- Orthopedic Surgery and Rehabilitation
- Pediatric Urology and Nephrology Studies
- Genetic and Kidney Cyst Diseases
- Tendon Structure and Treatment
Mayo Clinic in Florida
2022-2025
WinnMed
2018-2024
Mayo Clinic
2015-2023
Jacksonville College
2023
Mayo Clinic in Arizona
2018-2021
Rotterdam University of Applied Sciences
2015
Erasmus University Rotterdam
2011-2014
Erasmus MC
2011-2014
Hospices Civils de Lyon
2012
Hôpital Louis Pradel
2012
Several studies have linked codeletion of chromosome arms 1p/19q in low-grade gliomas (LGG) with positive response to treatment and longer progression-free survival. Hence, predicting status is crucial for effective planning LGG. In this study, we predict the from MR images using convolutional neural networks (CNN), which could be a non-invasive alternative surgical biopsy histopathological analysis. Our method consists three main steps: image registration, tumor segmentation, classification...
Deep learning (DL) is a popular method that used to perform many important tasks in radiology and medical imaging. Some forms of DL are able accurately segment organs (essentially, trace the boundaries, enabling volume measurements or calculation other properties). Other networks predict properties from regions an image-for instance, whether something malignant, molecular markers for tissue region, even prognostic markers. easier train than traditional machine methods, but requires more data...
Deep learning has shown great promise for improving medical image classification tasks. However, knowing what aspects of an the deep system uses or, in a manner speaking, sees to make its prediction is difficult.Within radiologic imaging context, we investigated utility methods designed identify features within images on which activates. In this study, developed classifier contrast enhancement phase from whole-slice CT data. We then used as easily interpretable explore class activation map...
Image segmentation plays an essential role in medicine for both diagnostic and interventional tasks. Segmentation approaches are either manual, semi-automated or fully-automated. Manual offers full control over the quality of results, but is tedious, time consuming prone to operator bias. Fully automated methods require no human effort, often deliver sub-optimal results without providing users with means make corrections. Semi-automated keep by interaction, main challenge offer a good...
Abstract Purpose Total kidney volume (TKV) is the most important imaging biomarker for quantifying severity of autosomal-dominant polycystic disease (ADPKD). 3D ultrasound (US) can accurately measure compared to 2D US; however, manual segmentation tedious and requires expert annotators. We investigated a deep learning-based approach automated TKV from US in ADPKD patients. Method used axially acquired US-kidney images 22 patients where each patient were scanned three times, resulting 132...
Patients with diabetes mellitus (DM) are at severely increased risk of developing atherosclerosis. Intraplaque neovascularization (IPN) and plaque ulceration markers the vulnerable plaque, which is an rupture may lead to cardiovascular events. The aim this study was assess prevalence subclinical carotid atherosclerosis, intraplaque (IPN), in asymptomatic patients DM. A total 51 DM underwent standard ultrasound conjunction contrast-enhanced (CEUS) IPN, ulceration. Subclinical atherosclerosis...
The objective of the present study is to develop and validate a fast, accurate, reproducible method that will increase improve institutional measurement total kidney volume thereby avoid higher costs, increased operator processing time, inherent subjectivity associated with manual contour tracing.We developed semiautomated segmentation approach, known as minimal interaction rapid organ (MIROS) method, which results in human during on MR images being reduced few minutes. This software tool...
To develop a deep learning model that segments intracranial structures on head CT scans.In this retrospective study, primary dataset containing 62 normal noncontrast scans from patients (mean age, 73 years; age range, 27-95 years) acquired between August and December 2018 was used for development. Eleven were manually annotated the axial oblique series. The split into 40 training, 10 validation, 12 testing. After initial eight configurations evaluated validation highest performing test...