AIMC Topic: Contrast Media
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Automatic myocardial segmentation in dynamic contrast enhanced perfusion MRI using Monte Carlo dropout in an encoder-decoder convolutional neural network.
Computer methods and programs in biomedicine
Oct 22, 2019
BACKGROUND AND OBJECTIVE: Cardiac perfusion magnetic resonance imaging (MRI) with first pass dynamic contrast enhancement (DCE) is a useful tool to identify perfusion defects in myocardial tissues. Automatic segmentation of the myocardium can lead to...
Cascade and Fusion of Multitask Convolutional Neural Networks for Detection of Thyroid Nodules in Contrast-Enhanced CT.
Computational intelligence and neuroscience
Oct 20, 2019
With the development of computed tomography (CT), the contrast-enhanced CT scan is widely used in the diagnosis of thyroid nodules. However, due to the artifacts and high complexity of thyroid CT images, traditional machine learning has difficulty in...
'Squeeze & excite' guided few-shot segmentation of volumetric images.
Medical image analysis
Oct 13, 2019
Deep neural networks enable highly accurate image segmentation, but require large amounts of manually annotated data for supervised training. Few-shot learning aims to address this shortcoming by learning a new class from a few annotated support exam...
Deep learning for World Health Organization grades of pancreatic neuroendocrine tumors on contrast-enhanced magnetic resonance images: a preliminary study.
International journal of computer assisted radiology and surgery
Sep 26, 2019
PURPOSE: The World Health Organization (WHO) grading system of pancreatic neuroendocrine tumor (PNET) plays an important role in the clinical decision. The rarity of PNET often negatively affects the radiological application of deep learning algorith...
Independent validation of machine learning in diagnosing breast Cancer on magnetic resonance imaging within a single institution.
Cancer imaging : the official publication of the International Cancer Imaging Society
Sep 18, 2019
BACKGROUND: As artificial intelligence methods for the diagnosis of disease advance, we aimed to evaluate machine learning in the predictive task of distinguishing between malignant and benign breast lesions on an independent clinical magnetic resona...
Post-processing radio-frequency signal based on deep learning method for ultrasonic microbubble imaging.
Biomedical engineering online
Sep 11, 2019
BACKGROUND: Improving imaging quality is a fundamental problem in ultrasound contrast agent imaging (UCAI) research. Plane wave imaging (PWI) has been deemed as a potential method for UCAI due to its' high frame rate and low mechanical index. High fr...
Automatic radiomic feature extraction using deep learning for angiographic parametric imaging of intracranial aneurysms.
Journal of neurointerventional surgery
Aug 23, 2019
BACKGROUND: Angiographic parametric imaging (API) is an imaging method that uses digital subtraction angiography (DSA) to characterize contrast media dynamics throughout the vasculature. This requires manual placement of a region of interest over a l...
Radiomics machine-learning signature for diagnosis of hepatocellular carcinoma in cirrhotic patients with indeterminate liver nodules.
European radiology
Aug 23, 2019
PURPOSE: To enhance clinician's decision-making by diagnosing hepatocellular carcinoma (HCC) in cirrhotic patients with indeterminate liver nodules using quantitative imaging features extracted from triphasic CT scans.
A self-supervised strategy for fully automatic segmentation of renal dynamic contrast-enhanced magnetic resonance images.
Medical physics
Aug 16, 2019
PURPOSE: An automated accurate segmentation for dynamic contrast-enhanced magnetic resonance (DCE-MR) image sequences is essential for quantification of renal function. A self-supervised strategy is proposed for fully automatic segmentation of the re...
Deep learning for automated segmentation of pelvic muscles, fat, and bone from CT studies for body composition assessment.
Skeletal radiology
Aug 8, 2019
OBJECTIVE: To develop a deep convolutional neural network (CNN) to automatically segment an axial CT image of the pelvis for body composition measures. We hypothesized that a deep CNN approach would achieve high accuracy when compared to manual segme...
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