AIMC Topic: Magnetic Resonance Imaging
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MD-UNET: Multi-input dilated U-shape neural network for segmentation of bladder cancer.
Computational biology and chemistry
May 20, 2021
Accurate segmentation of the tumour area is crucial for the treatment and prognosis of patients with bladder cancer. However, the complex information from the MRI image poses an important challenge for us to accurately segment the lesion, for example...
Integrative learning for population of dynamic networks with covariates.
NeuroImage
May 20, 2021
Although there is a rapidly growing literature on dynamic connectivity methods, the primary focus has been on separate network estimation for each individual, which fails to leverage common patterns of information. We propose novel graph-theoretic ap...
Radiomics and deep learning methods in expanding the use of screening breast MRI.
European radiology
May 20, 2021
• The use of screening breast MRI is expanding beyond high-risk women to include intermediate- and average-risk women.• The study by Pötsch et al uses a radiomics-based method to decrease the number of benign biopsies while maintaining high sensitivi...
High-frequency conductivity at Larmor-frequency in human brain using moving local window multilayer perceptron neural network.
PloS one
May 20, 2021
Magnetic resonance electrical properties tomography (MREPT) aims to visualize the internal high-frequency conductivity distribution at Larmor frequency using the B1 transceive phase data. From the magnetic field perturbation by the electrical field a...
Artificial Intelligence in magnetic Resonance guided Radiotherapy: Medical and physical considerations on state of art and future perspectives.
Physica medica : PM : an international journal devoted to the applications of physics to medicine and biology : official journal of the Italian Association of Biomedical Physics (AIFB)
May 19, 2021
Over the last years, technological innovation in Radiotherapy (RT) led to the introduction of Magnetic Resonance-guided RT (MRgRT) systems. Due to the higher soft tissue contrast compared to on-board CT-based systems, MRgRT is expected to significant...
Deep learning-based cardiac cine segmentation: Transfer learning application to 7T ultrahigh-field MRI.
Magnetic resonance in medicine
May 18, 2021
PURPOSE: Artificial neural networks show promising performance in automatic segmentation of cardiac MRI. However, training requires large amounts of annotated data and generalization to different vendors, field strengths, sequence parameters, and pat...
MRI and CT bladder segmentation from classical to deep learning based approaches: Current limitations and lessons.
Computers in biology and medicine
May 18, 2021
Precise determination and assessment of bladder cancer (BC) extent of muscle invasion involvement guides proper risk stratification and personalized therapy selection. In this context, segmentation of both bladder walls and cancer are of pivotal impo...
A deep learning model for detection of cervical spinal cord compression in MRI scans.
Scientific reports
May 18, 2021
Magnetic Resonance Imaging (MRI) evidence of spinal cord compression plays a central role in the diagnosis of degenerative cervical myelopathy (DCM). There is growing recognition that deep learning models may assist in addressing the increasing volum...
Atherosclerotic Burden and Remodeling Patterns of the Popliteal Artery as Detected in the Magnetic Resonance Imaging Osteoarthritis Initiative Data Set.
Journal of the American Heart Association
May 17, 2021
Background An artificial intelligence vessel segmentation tool, Fully Automated and Robust Analysis Technique for Popliteal Artery Evaluation (FRAPPE), was used to analyze a large databank of popliteal arteries imaged through the OAI (Osteoarthritis ...
LCC-Net: A Lightweight Cross-Consistency Network for Semisupervised Cardiac MR Image Segmentation.
Computational and mathematical methods in medicine
May 17, 2021
Semantic segmentation plays a crucial role in cardiac magnetic resonance (MR) image analysis. Although supervised deep learning methods have made significant performance improvements, they highly rely on a large amount of pixel-wise annotated data, w...
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