AIMC Topic:
Magnetic Resonance Imaging
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Automated identification of piglet brain tissue from MRI images using Region-based Convolutional Neural Networks.
PloS one
May 11, 2023
Magnetic resonance imaging is an important tool for characterizing volumetric changes of the piglet brain during development. Typically, an early step of an imaging analysis pipeline is brain extraction, or skull stripping. Brain extractions are usua...
Boosting multiple sclerosis lesion segmentation through attention mechanism.
Computers in biology and medicine
May 10, 2023
Magnetic resonance imaging is a fundamental tool to reach a diagnosis of multiple sclerosis and monitoring its progression. Although several attempts have been made to segment multiple sclerosis lesions using artificial intelligence, fully automated ...
Benign vs malignant vertebral compression fractures with MRI: a comparison between automatic deep learning network and radiologist's assessment.
European radiology
May 10, 2023
OBJECTIVE: To test the diagnostic performance of a deep-learning Two-Stream Compare and Contrast Network (TSCCN) model for differentiating benign and malignant vertebral compression fractures (VCFs) based on MRI.
Automated Placement of Scan and Pre-Scan Volumes for Breast MRI Using a Convolutional Neural Network.
Tomography (Ann Arbor, Mich.)
May 10, 2023
Graphically prescribed patient-specific imaging volumes and local pre-scan volumes are routinely placed by MRI technologists to optimize image quality. However, manual placement of these volumes by MR technologists is time-consuming, tedious, and sub...
Deep learning-based PET/MR radiomics for the classification of annualized relapse rate in multiple sclerosis.
Multiple sclerosis and related disorders
May 9, 2023
Background Annualized Relapse Rate (ARR) is one of the most important indicators of disease progression in patients with Multiple Sclerosis (MS). However, imaging markers that can effectively predict ARR are currently unavailable. In this study, we d...
Imaging of early-stage osteoarthritis: the needs and challenges for diagnosis and classification.
Skeletal radiology
May 8, 2023
In an effort to boost the development of new management strategies for OA, there is currently a shift in focus towards the diagnosis and treatment of early-stage OA. It is important to distinguish diagnosis from classification of early-stage OA. Diag...
Discrimination Between Glioblastoma and Solitary Brain Metastasis Using Conventional MRI and Diffusion-Weighted Imaging Based on a Deep Learning Algorithm.
Journal of digital imaging
May 8, 2023
This study aims to develop and validate a deep learning (DL) model to differentiate glioblastoma from single brain metastasis (BM) using conventional MRI combined with diffusion-weighted imaging (DWI). Preoperative conventional MRI and DWI of 202 pat...
Deep learning referral suggestion and tumour discrimination using explainable artificial intelligence applied to multiparametric MRI.
European radiology
May 8, 2023
OBJECTIVES: An appropriate and fast clinical referral suggestion is important for intra-axial mass-like lesions (IMLLs) in the emergency setting. We aimed to apply an interpretable deep learning (DL) system to multiparametric MRI to obtain clinical r...
Automatic detection and recognition of nasopharynx gross tumour volume (GTVnx) by deep learning for nasopharyngeal cancer radiotherapy through magnetic resonance imaging.
Radiation oncology (London, England)
May 8, 2023
BACKGROUND: In this study, we propose the deep learning model-based framework to automatically delineate nasopharynx gross tumor volume (GTVnx) in MRI images.
Muscle magnetic resonance characterization of STIM1 tubular aggregate myopathy using unsupervised learning.
PloS one
May 8, 2023
PURPOSE: Congenital myopathies are a heterogeneous group of diseases affecting the skeletal muscles and characterized by high clinical, genetic, and histological variability. Magnetic Resonance (MR) is a valuable tool for the assessment of involved m...
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