AIMC Journal:
IEEE transactions on medical imaging

Showing 371 to 380 of 687 articles

Mutual Information-Based Disentangled Neural Networks for Classifying Unseen Categories in Different Domains: Application to Fetal Ultrasound Imaging.

IEEE transactions on medical imaging Feb 2, 2021
Deep neural networks exhibit limited generalizability across images with different entangled domain features and categorical features. Learning generalizable features that can form universal categorical decision boundaries across domains is an intere...
Fetus Ultrasonography, Prenatal Supervised Machine Learning Ultrasonography Pregnancy Female Neural Networks, Computer Humans
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CA-Net: Comprehensive Attention Convolutional Neural Networks for Explainable Medical Image Segmentation.

IEEE transactions on medical imaging Feb 2, 2021
Accurate medical image segmentation is essential for diagnosis and treatment planning of diseases. Convolutional Neural Networks (CNNs) have achieved state-of-the-art performance for automatic medical image segmentation. However, they are still chall...
Neural Networks, Computer Image Processing, Computer-Assisted Magnetic Resonance Imaging
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Weakly Supervised Deep Learning-Based Optical Coherence Tomography Angiography.

IEEE transactions on medical imaging Feb 2, 2021
Optical coherence tomography angiography (OCTA) is a promising imaging modality for microvasculature studies. Deep learning networks have been widely applied in the field of OCTA reconstruction, benefiting from its powerful mapping capability among i...
Animals Deep Learning Humans Angiography Fluorescein Angiography Tomography, Optical Coherence
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Spherical-Patches Extraction for Deep-Learning-Based Critical Points Detection in 3D Neuron Microscopy Images.

IEEE transactions on medical imaging Feb 2, 2021
Digital reconstruction of neuronal structures is very important to neuroscience research. Many existing reconstruction algorithms require a set of good seed points. 3D neuron critical points, including terminations, branch points and cross-over point...
Microscopy Neural Networks, Computer Neurons Imaging, Three-Dimensional Deep Learning
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A Curvature-Enhanced Random Walker Segmentation Method for Detailed Capture of 3D Cell Surface Membranes.

IEEE transactions on medical imaging Feb 2, 2021
High-resolution 3D microscopy is a fast advancing field and requires new techniques in image analysis to handle these new datasets. In this work, we focus on detailed 3D segmentation of Dictyostelium cells undergoing macropinocytosis captured on an i...
Neural Networks, Computer Microscopy Image Processing, Computer-Assisted Imaging, Three-Dimensional Dictyostelium
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Non-Rigid Respiratory Motion Estimation of Whole-Heart Coronary MR Images Using Unsupervised Deep Learning.

IEEE transactions on medical imaging Dec 29, 2020
Non-rigid motion-corrected reconstruction has been proposed to account for the complex motion of the heart in free-breathing 3D coronary magnetic resonance angiography (CMRA). This reconstruction framework requires efficient and accurate estimation o...
Coronary Vessels Magnetic Resonance Angiography Image Processing, Computer-Assisted Deep Learning Motion Humans Imaging, Three-Dimensional Heart Coronary Angiography
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A CT-Based Automated Algorithm for Airway Segmentation Using Freeze-and-Grow Propagation and Deep Learning.

IEEE transactions on medical imaging Dec 29, 2020
Chronic obstructive pulmonary disease (COPD) is a common lung disease, and quantitative CT-based bronchial phenotypes are of increasing interest as a means of exploring COPD sub-phenotypes, establishing disease progression, and evaluating interventio...
Deep Learning Reproducibility of Results Tomography, X-Ray Computed Algorithms Lung
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DetexNet: Accurately Diagnosing Frequent and Challenging Pediatric Malignant Tumors.

IEEE transactions on medical imaging Dec 29, 2020
The most frequent extracranial solid tumors of childhood, named peripheral neuroblastic tumors (pNTs), are very challenging to diagnose due to their diversified categories and varying forms. Auxiliary diagnosis methods of such pediatric malignant can...
Neoplasms Child Humans Neural Networks, Computer
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Unpaired Training of Deep Learning tMRA for Flexible Spatio-Temporal Resolution.

IEEE transactions on medical imaging Dec 29, 2020
Time-resolved MR angiography (tMRA) has been widely used for dynamic contrast enhanced MRI (DCE-MRI) due to its highly accelerated acquisition. In tMRA, the periphery of the k -space data are sparsely sampled so that neighbouring frames can be merged...
Computer Simulation Magnetic Resonance Imaging Angiography Deep Learning
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Wasserstein GANs for MR Imaging: From Paired to Unpaired Training.

IEEE transactions on medical imaging Dec 29, 2020
Lack of ground-truth MR images impedes the common supervised training of neural networks for image reconstruction. To cope with this challenge, this article leverages unpaired adversarial training for reconstruction networks, where the inputs are und...
Image Processing, Computer-Assisted Magnetic Resonance Imaging Neural Networks, Computer
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