AIMC Topic: Magnetic Resonance Imaging
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Artificial Intelligence Algorithm in Classification and Recognition of Primary Hepatic Carcinoma Images under Magnetic Resonance Imaging.
Contrast media & molecular imaging
Jun 7, 2022
This study aimed to discuss the application value of the bias field correction algorithm in magnetic resonance imaging (MRI) images of patients with primary hepatic carcinoma (PHC). In total, 52 patients with PHC were selected as the experimental gro...
A combination model of AD biomarkers revealed by machine learning precisely predicts Alzheimer's dementia: China Aging and Neurodegenerative Initiative (CANDI) study.
Alzheimer's & dementia : the journal of the Alzheimer's Association
Jun 6, 2022
INTRODUCTION: To test the utility of the "A/T/N" system in the Chinese population, we study core Alzheimer's disease (AD) biomarkers in a newly established Chinese cohort.
Deep Learning Algorithm-Based Magnetic Resonance Imaging Feature-Guided Serum Bile Acid Profile and Perinatal Outcomes in Intrahepatic Cholestasis of Pregnancy.
Computational and mathematical methods in medicine
Jun 6, 2022
This study was aimed to explore magnetic resonance imaging (MRI) based on deep learning belief network model in evaluating serum bile acid profile and adverse perinatal outcomes of intrahepatic cholestasis of pregnancy (ICP) patients. Fifty ICP pregn...
MASS: Modality-collaborative semi-supervised segmentation by exploiting cross-modal consistency from unpaired CT and MRI images.
Medical image analysis
Jun 5, 2022
Training deep segmentation models for medical images often requires a large amount of labeled data. To tackle this issue, semi-supervised segmentation has been employed to produce satisfactory delineation results with affordable labeling cost. Howeve...
A Prior Knowledge-Guided, Deep Learning-Based Semiautomatic Segmentation for Complex Anatomy on Magnetic Resonance Imaging.
International journal of radiation oncology, biology, physics
Jun 4, 2022
PURPOSE: Despite recent substantial improvement in autosegmentation using deep learning (DL) methods, labor-intensive and time-consuming slice-by-slice manual editing is often needed, particularly for complex anatomy (eg, abdominal organs). This work...
End-systole and end-diastole detection in short axis cine MRI using a fully convolutional neural network with dilated convolutions.
Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society
Jun 3, 2022
The correct assessment and characterization of heart anatomy and functionality is usually done through inspection of magnetic resonance image cine sequences. In the clinical setting it is especially important to determine the state of the left ventri...
Multi-Level Functional Connectivity Fusion Classification Framework for Brain Disease Diagnosis.
IEEE journal of biomedical and health informatics
Jun 3, 2022
Brain disease diagnosis is a new hotspot in the cross research of artificial intelligence and neuroscience. Quantitative analysis of functional magnetic resonance imaging (fMRI) data can provide valuable biomarkers that contributes to clinical diagno...
⊥-loss: A symmetric loss function for magnetic resonance imaging reconstruction and image registration with deep learning.
Medical image analysis
Jun 2, 2022
Convolutional neural networks (CNNs) are increasingly adopted in medical imaging, e.g., to reconstruct high-quality images from undersampled magnetic resonance imaging (MRI) acquisitions or estimate subject motion during an examination. MRI is natura...
NPBDREG: Uncertainty assessment in diffeomorphic brain MRI registration using a non-parametric Bayesian deep-learning based approach.
Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society
Jun 2, 2022
Quantification of uncertainty in deep-neural-networks (DNN) based image registration algorithms plays a critical role in the deployment of image registration algorithms for clinical applications such as surgical planning, intraoperative guidance, and...
Towards fully automated segmentation of rat cardiac MRI by leveraging deep learning frameworks.
Scientific reports
Jun 2, 2022
Automated segmentation of human cardiac magnetic resonance datasets has been steadily improving during recent years. Similar applications would be highly useful to improve and speed up the studies of cardiac function in rodents in the preclinical con...
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