AIMC Topic: Magnetic Resonance Imaging
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Editorial for "Fully Automated MRI Segmentation and Volumetric Measurement of Intracranial Meningioma Using Deep Learning".
Journal of magnetic resonance imaging : JMRI
Jul 1, 2022
Fully Automated MRI Segmentation and Volumetric Measurement of Intracranial Meningioma Using Deep Learning.
Journal of magnetic resonance imaging : JMRI
Jul 1, 2022
BACKGROUND: Accurate and rapid measurement of the MRI volume of meningiomas is essential in clinical practice to determine the growth rate of the tumor. Imperfect automation and disappointing performance for small meningiomas of previous automated vo...
Deep Learning-Enhanced Parallel Imaging and Simultaneous Multislice Acceleration Reconstruction in Knee MRI.
Investigative radiology
Jul 1, 2022
OBJECTIVES: This study aimed to examine various combinations of parallel imaging (PI) and simultaneous multislice (SMS) acceleration imaging using deep learning (DL)-enhanced and conventional reconstruction. The study also aimed at comparing the diag...
All-Around Real Label Supervision: Cyclic Prototype Consistency Learning for Semi-Supervised Medical Image Segmentation.
IEEE journal of biomedical and health informatics
Jul 1, 2022
Semi-supervised learning has substantially advanced medical image segmentation since it alleviates the heavy burden of acquiring the costly expert-examined annotations. Especially, the consistency-based approaches have attracted more attention for th...
Cross-Modality Multi-Atlas Segmentation via Deep Registration and Label Fusion.
IEEE journal of biomedical and health informatics
Jul 1, 2022
Multi-atlas segmentation (MAS) is a promising framework for medical image segmentation. Generally, MAS methods register multiple atlases, i.e., medical images with corresponding labels, to a target image; and the transformed atlas labels can be combi...
Diagnosis of Brain Tumor Using Light Weight Deep Learning Model with Fine-Tuning Approach.
Computational and mathematical methods in medicine
Jul 1, 2022
Brain cancer is a rare and deadly disease with a slim chance of survival. One of the most important tasks for neurologists and radiologists is to detect brain tumors early. Recent claims have been made that computer-aided diagnosis-based systems can ...
Combined diagnosis of multiparametric MRI-based deep learning models facilitates differentiating triple-negative breast cancer from fibroadenoma magnetic resonance BI-RADS 4 lesions.
Journal of cancer research and clinical oncology
Jun 30, 2022
PURPOSE: To investigate the value of the combined diagnosis of multiparametric MRI-based deep learning models to differentiate triple-negative breast cancer (TNBC) from fibroadenoma magnetic resonance Breast Imaging-Reporting and Data System category...
Self-Supervised Learning for Few-Shot Medical Image Segmentation.
IEEE transactions on medical imaging
Jun 30, 2022
Fully-supervised deep learning segmentation models are inflexible when encountering new unseen semantic classes and their fine-tuning often requires significant amounts of annotated data. Few-shot semantic segmentation (FSS) aims to solve this inflex...
Unsupervised MRI Reconstruction via Zero-Shot Learned Adversarial Transformers.
IEEE transactions on medical imaging
Jun 30, 2022
Supervised reconstruction models are characteristically trained on matched pairs of undersampled and fully-sampled data to capture an MRI prior, along with supervision regarding the imaging operator to enforce data consistency. To reduce supervision ...
The Interfield Strength Agreement of Left Ventricular Strain Measurements at 1.5 T and 3 T Using Cardiac MRI Feature Tracking.
Journal of magnetic resonance imaging : JMRI
Jun 29, 2022
BACKGROUND: Left ventricular (LV) strain measurements can be derived using cardiac MRI from routinely acquired balanced steady-state free precession (bSSFP) cine images.
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