AIMC Topic: Magnetic Resonance Imaging
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Fuzzy Clustering Algorithm Based on Improved Global Best-Guided Artificial Bee Colony with New Search Probability Model for Image Segmentation.
Sensors (Basel, Switzerland)
Nov 18, 2022
Clustering using fuzzy C-means (FCM) is a soft segmentation method that has been extensively investigated and successfully implemented in image segmentation. FCM is useful in various aspects, such as the segmentation of grayscale images. However, FCM...
Uncertainty-aware self-supervised neural network for livermapping with relaxation constraint.
Physics in medicine and biology
Nov 18, 2022
.T1ρmapping is a promising quantitative MRI technique for the non-invasive assessment of tissue properties. Learning-based approaches can mapT1ρfrom a reduced number ofT1ρweighted images but requires significant amounts of high-quality training data....
Multifunctional microrobot with real-time visualization and magnetic resonance imaging for chemoembolization therapy of liver cancer.
Science advances
Nov 18, 2022
Microrobots that can be precisely guided to target lesions have been studied for in vivo medical applications. However, existing microrobots have challenges in vivo such as biocompatibility, biodegradability, actuation module, and intra- and postoper...
An Ensemble of Deep Learning Enabled Brain Stroke Classification Model in Magnetic Resonance Images.
Journal of healthcare engineering
Nov 18, 2022
Brain stroke is a major cause of global death and it necessitates earlier identification process to reduce the mortality rate. Magnetic resonance imaging (MRI) techniques is a commonly available imaging modality used to diagnose brain stroke. Present...
Generative adversarial network constrained multiple loss autoencoder: A deep learning-based individual atrophy detection for Alzheimer's disease and mild cognitive impairment.
Human brain mapping
Nov 17, 2022
Exploring individual brain atrophy patterns is of great value in precision medicine for Alzheimer's disease (AD) and mild cognitive impairment (MCI). However, the current individual brain atrophy detection models are deficient. Here, we proposed a fr...
On the usability of synthetic data for improving the robustness of deep learning-based segmentation of cardiac magnetic resonance images.
Medical image analysis
Nov 17, 2022
Deep learning-based segmentation methods provide an effective and automated way for assessing the structure and function of the heart in cardiac magnetic resonance (CMR) images. However, despite their state-of-the-art performance on images acquired f...
Zoom-In Neural Network Deep-Learning Model for Alzheimer's Disease Assessments.
Sensors (Basel, Switzerland)
Nov 17, 2022
Deep neural networks have been successfully applied to generate predictive patterns from medical and diagnostic data. This paper presents an approach for assessing persons with Alzheimer's disease (AD) mild cognitive impairment (MCI), compared with n...
Identification of texture MRI brain abnormalities on first-episode psychosis and clinical high-risk subjects using explainable artificial intelligence.
Translational psychiatry
Nov 16, 2022
Structural MRI studies in first-episode psychosis and the clinical high-risk state have consistently shown volumetric abnormalities. Aim of the present study was to introduce radiomics texture features in identification of psychosis. Radiomics textur...
MR-self Noise2Noise: self-supervised deep learning-based image quality improvement of submillimeter resolution 3D MR images.
European radiology
Nov 15, 2022
OBJECTIVES: The study aimed to develop a deep neural network (DNN)-based noise reduction and image quality improvement by only using routine clinical scans and evaluate its performance in 3D high-resolution MRI.
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