AIMC Journal:
IEEE transactions on medical imaging

Showing 241 to 250 of 687 articles

Automatic Liver Tumor Segmentation on Dynamic Contrast Enhanced MRI Using 4D Information: Deep Learning Model Based on 3D Convolution and Convolutional LSTM.

IEEE transactions on medical imaging Sep 30, 2022
OBJECTIVE: Accurate segmentation of liver tumors, which could help physicians make appropriate treatment decisions and assess the effectiveness of surgical treatment, is crucial for the clinical diagnosis of liver cancer. In this study, we propose a ...
Image Processing, Computer-Assisted Humans Deep Learning Carcinoma, Hepatocellular Liver Neoplasms Neural Networks, Computer Magnetic Resonance Imaging
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Sam's Net: A Self-Augmented Multistage Deep-Learning Network for End-to-End Reconstruction of Limited Angle CT.

IEEE transactions on medical imaging Sep 30, 2022
Limited angle reconstruction is a typical ill-posed problem in computed tomography (CT). Given incomplete projection data, images reconstructed by conventional analytical algorithms and iterative methods suffer from severe structural distortions and ...
Neural Networks, Computer Artifacts Image Processing, Computer-Assisted Phantoms, Imaging Tomography, X-Ray Computed Deep Learning Algorithms
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Fully-Automated Spike Detection and Dipole Analysis of Epileptic MEG Using Deep Learning.

IEEE transactions on medical imaging Sep 30, 2022
Magnetoencephalography (MEG) is a useful tool for clinically evaluating the localization of interictal spikes. Neurophysiologists visually identify spikes from the MEG waveforms and estimate the equivalent current dipoles (ECD). However, presently, t...
Epilepsy Electroencephalography Magnetoencephalography Deep Learning Image Processing, Computer-Assisted Sensitivity and Specificity Humans
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Echocardiography Segmentation With Enforced Temporal Consistency.

IEEE transactions on medical imaging Sep 30, 2022
Convolutional neural networks (CNN) have demonstrated their ability to segment 2D cardiac ultrasound images. However, despite recent successes according to which the intra-observer variability on end-diastole and end-systole images has been reached, ...
Echocardiography Image Processing, Computer-Assisted Neural Networks, Computer Observer Variation Heart Humans
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MOOD 2020: A Public Benchmark for Out-of-Distribution Detection and Localization on Medical Images.

IEEE transactions on medical imaging Sep 30, 2022
Detecting Out-of-Distribution (OoD) data is one of the greatest challenges in safe and robust deployment of machine learning algorithms in medicine. When the algorithms encounter cases that deviate from the distribution of the training data, they oft...
Humans Machine Learning Benchmarking Algorithms
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Deep-Learning-Based Fast Optical Coherence Tomography (OCT) Image Denoising for Smart Laser Osteotomy.

IEEE transactions on medical imaging Sep 30, 2022
Laser osteotomy promises precise cutting and minor bone tissue damage. We proposed Optical Coherence Tomography (OCT) to monitor the ablation process toward our smart laser osteotomy approach. The OCT image is helpful to identify tissue type and prov...
Lasers Tomography, Optical Coherence Image Processing, Computer-Assisted Deep Learning Osteotomy Artifacts
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CAR-Net: A Deep Learning-Based Deformation Model for 3D/2D Coronary Artery Registration.

IEEE transactions on medical imaging Sep 30, 2022
Percutaneous coronary intervention is widely applied for the treatment of coronary artery disease under the guidance of X-ray coronary angiography (XCA) image. However, the projective nature of XCA causes the loss of 3D structural information, which ...
Algorithms Deep Learning Coronary Vessels Coronary Angiography Imaging, Three-Dimensional
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SPHARM-Net: Spherical Harmonics-Based Convolution for Cortical Parcellation.

IEEE transactions on medical imaging Sep 30, 2022
We present a spherical harmonics-based convolutional neural network (CNN) for cortical parcellation, which we call SPHARM-Net. Recent advances in CNNs offer cortical parcellation on a fine-grained triangle mesh of the cortex. Yet, most CNNs designed ...
Neural Networks, Computer
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Super-Resolved Microbubble Localization in Single-Channel Ultrasound RF Signals Using Deep Learning.

IEEE transactions on medical imaging Aug 31, 2022
Recently, super-resolution ultrasound imaging with ultrasound localization microscopy (ULM) has received much attention. However, ULM relies on low concentrations of microbubbles in the blood vessels, ultimately resulting in long acquisition times. H...
Microscopy Ultrasonography Deep Learning Microbubbles Contrast Media Radio Waves
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Learning From Synthetic CT Images via Test-Time Training for Liver Tumor Segmentation.

IEEE transactions on medical imaging Aug 31, 2022
Automatic liver tumor segmentation could offer assistance to radiologists in liver tumor diagnosis, and its performance has been significantly improved by recent deep learning based methods. These methods rely on large-scale well-annotated training d...
Neural Networks, Computer Tomography, X-Ray Computed Humans Abdomen Image Processing, Computer-Assisted Liver Neoplasms
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