AIMC Journal:
IEEE transactions on medical imaging

Showing 481 to 490 of 687 articles

Toward Automated 3D Spine Reconstruction from Biplanar Radiographs Using CNN for Statistical Spine Model Fitting.

IEEE transactions on medical imaging May 3, 2019
To date, 3D spine reconstruction from biplanar radiographs involves intensive user supervision and semi-automated methods that are time-consuming and not effective in clinical routine. This paper proposes a new, fast, and automated 3D spine reconstru...
Neural Networks, Computer Algorithms Adolescent Humans Radiography Spine Scoliosis Imaging, Three-Dimensional Principal Component Analysis
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Weakly Supervised Estimation of Shadow Confidence Maps in Fetal Ultrasound Imaging.

IEEE transactions on medical imaging Apr 25, 2019
Detecting acoustic shadows in ultrasound images is important in many clinical and engineering applications. Real-time feedback of acoustic shadows can guide sonographers to a standardized diagnostic viewing plane with minimal artifacts and can provid...
Fetus Deep Learning Image Processing, Computer-Assisted Supervised Machine Learning Female Ultrasonography, Prenatal Humans Algorithms Pregnancy
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Deep Attentive Features for Prostate Segmentation in 3D Transrectal Ultrasound.

IEEE transactions on medical imaging Apr 25, 2019
Automatic prostate segmentation in transrectal ultrasound (TRUS) images is of essential importance for image-guided prostate interventions and treatment planning. However, developing such automatic solutions remains very challenging due to the missin...
Humans Male Deep Learning Neural Networks, Computer Prostatic Neoplasms Prostate Ultrasonography Imaging, Three-Dimensional
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Augmentation of CBCT Reconstructed From Under-Sampled Projections Using Deep Learning.

IEEE transactions on medical imaging Apr 23, 2019
Edges tend to be over-smoothed in total variation (TV) regularized under-sampled images. In this paper, symmetric residual convolutional neural network (SR-CNN), a deep learning based model, was proposed to enhance the sharpness of edges and detailed...
Lung Neoplasms Phantoms, Imaging Algorithms Lung Cone-Beam Computed Tomography Image Processing, Computer-Assisted Deep Learning Humans
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Deep Q Learning Driven CT Pancreas Segmentation With Geometry-Aware U-Net.

IEEE transactions on medical imaging Apr 16, 2019
The segmentation of pancreas is important for medical image analysis, yet it faces great challenges of class imbalance, background distractions, and non-rigid geometrical features. To address these difficulties, we introduce a deep Q network (DQN) dr...
Tomography, X-Ray Computed Deep Learning Pancreas Image Processing, Computer-Assisted Humans Algorithms Databases, Factual
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Longitudinal Prediction of Infant Diffusion MRI Data via Graph Convolutional Adversarial Networks.

IEEE transactions on medical imaging Apr 15, 2019
Missing data is a common problem in longitudinal studies due to subject dropouts and failed scans. We present a graph-based convolutional neural network to predict missing diffusion MRI data. In particular, we consider the relationships between sampl...
Brain Infant, Newborn Image Interpretation, Computer-Assisted Humans Neural Networks, Computer Child Development Diffusion Magnetic Resonance Imaging
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Approximating the Ideal Observer and Hotelling Observer for Binary Signal Detection Tasks by Use of Supervised Learning Methods.

IEEE transactions on medical imaging Apr 15, 2019
It is widely accepted that the optimization of medical imaging system performance should be guided by task-based measures of image quality (IQ). Task-based measures of IQ quantify the ability of an observer to perform a specific task, such as detecti...
Bayes Theorem Signal Processing, Computer-Assisted Supervised Machine Learning Image Processing, Computer-Assisted Diagnostic Imaging Databases, Factual
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Learning to Reconstruct Computed Tomography Images Directly From Sinogram Data Under A Variety of Data Acquisition Conditions.

IEEE transactions on medical imaging Apr 11, 2019
Computed tomography (CT) is widely used in medical diagnosis and non-destructive detection. Image reconstruction in CT aims to accurately recover pixel values from measured line integrals, i.e., the summed pixel values along straight lines. Provided ...
Phantoms, Imaging Image Processing, Computer-Assisted Abdomen Head Radiography, Thoracic Coronary Vessels Humans Tomography, X-Ray Computed Deep Learning
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Intelligent Labeling Based on Fisher Information for Medical Image Segmentation Using Deep Learning.

IEEE transactions on medical imaging Mar 27, 2019
Deep convolutional neural networks (CNN) have recently achieved superior performance at the task of medical image segmentation compared to classic models. However, training a generalizable CNN requires a large amount of training data, which is diffic...
Deep Learning Child, Preschool Magnetic Resonance Imaging Image Processing, Computer-Assisted Brain Infant, Newborn Infant Humans Algorithms
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Training Convolutional Neural Networks and Compressed Sensing End-to-End for Microscopy Cell Detection.

IEEE transactions on medical imaging Mar 25, 2019
Automated cell detection and localization from microscopy images are significant tasks in biomedical research and clinical practice. In this paper, we design a new cell detection and localization algorithm that combines deep convolutional neural netw...
Databases, Factual Image Processing, Computer-Assisted Cytological Techniques Neural Networks, Computer Algorithms Microscopy Mitosis Humans
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