AIMC Topic: Magnetic Resonance Imaging
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Importance of neural network complexity for the automatic segmentation of individual thigh muscles in MRI images from patients with neuromuscular diseases.
Magma (New York, N.Y.)
Jan 11, 2025
OBJECTIVE: Segmentation of individual thigh muscles in MRI images is essential for monitoring neuromuscular diseases and quantifying relevant biomarkers such as fat fraction (FF). Deep learning approaches such as U-Net have demonstrated effectiveness...
Humans
Neural Networks, Computer
Algorithms
Deep Learning
Image Processing, Computer-Assisted
Magnetic Resonance Imaging
Muscle, Skeletal
Aged
Databases, Factual
Female
Adult
Reproducibility of Results
Male
Middle Aged
Adipose Tissue
Thigh
Image Interpretation, Computer-Assisted
Neuromuscular Diseases
A systematic review of deep learning in MRI-based cerebral vascular occlusion-based brain diseases.
Neuroscience
Jan 11, 2025
Neurological disorders, including cerebral vascular occlusions and strokes, present a major global health challenge due to their high mortality rates and long-term disabilities. Early diagnosis, particularly within the first hours, is crucial for pre...
Visualizing Preosteoarthritis: Updates on UTE-Based Compositional MRI and Deep Learning Algorithms.
Journal of magnetic resonance imaging : JMRI
Jan 10, 2025
Osteoarthritis (OA) is heterogeneous and involves structural changes in the whole joint, such as cartilage, meniscus/labrum, ligaments, and tendons, mainly with short T2 relaxation times. Detecting OA before the onset of irreversible changes is cruci...
Semi-Supervised Learning Allows for Improved Segmentation With Reduced Annotations of Brain Metastases Using Multicenter MRI Data.
Journal of magnetic resonance imaging : JMRI
Jan 10, 2025
BACKGROUND: Deep learning-based segmentation of brain metastases relies on large amounts of fully annotated data by domain experts. Semi-supervised learning offers potential efficient methods to improve model performance without excessive annotation ...
Multi-center brain age prediction via dual-modality fusion convolutional network.
Medical image analysis
Jan 10, 2025
Accurate prediction of brain age is crucial for identifying deviations between typical individual brain development trajectories and neuropsychiatric disease progression. Although current research has made progress, the effective application of brain...
BMA-Net: A 3D bidirectional multi-scale feature aggregation network for prostate region segmentation.
Computer methods and programs in biomedicine
Jan 10, 2025
BACKGROUND AND OBJECTIVE: Accurate segmentation of the prostate region in magnetic resonance imaging (MRI) is crucial for prostate-related diagnoses. Recent studies have incorporated Transformers into prostate region segmentation to better capture lo...
Development and routine implementation of deep learning algorithm for automatic brain metastases segmentation on MRI for RANO-BM criteria follow-up.
NeuroImage
Jan 10, 2025
RATIONALE AND OBJECTIVES: The RANO-BM criteria, which employ a one-dimensional measurement of the largest diameter, are imperfect due to the fact that the lesion volume is neither isotropic nor homogeneous. Furthermore, this approach is inherently ti...
A hybrid explainable model based on advanced machine learning and deep learning models for classifying brain tumors using MRI images.
Scientific reports
Jan 10, 2025
Brain tumors present a significant global health challenge, and their early detection and accurate classification are crucial for effective treatment strategies. This study presents a novel approach combining a lightweight parallel depthwise separabl...
Deep learning algorithms enable MRI-based scapular morphology analysis with values comparable to CT-based assessments.
Scientific reports
Jan 10, 2025
Scapular morphological attributes show promise as prognostic indicators of retear following rotator cuff repair. Current evaluation techniques using single-slice magnetic-resonance imaging (MRI) are, however, prone to error, while more accurate compu...
Hypothalamic atrophy in primary lateral sclerosis, assessed by convolutional neural network-based automatic segmentation.
Scientific reports
Jan 10, 2025
Primary lateral sclerosis (PLS) is a motor neuron disease (MND) which mainly affects upper motor neurons. Within the MND spectrum, PLS is much more slowly progressive than amyotrophic laterals sclerosis (ALS). `Classical` ALS is characterized by cata...
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