AIMC Journal:
Medical physics

Showing 711 to 720 of 759 articles

Metal artifact reduction for practical dental computed tomography by improving interpolation-based reconstruction with deep learning.

Medical physics Dec 1, 2019
PURPOSE: Metal artifact is a quite common problem in diagnostic dental computed tomography (CT) images. Due to the high attenuation of heavy materials such as metal, severe global artifacts can occur in reconstructions. Typical metal artifact reducti...
Artifacts Dentistry Metals Image Processing, Computer-Assisted Supervised Machine Learning Deep Learning
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A two-dimensional feasibility study of deep learning-based feature detection and characterization directly from CT sinograms.

Medical physics Dec 1, 2019
Machine Learning, especially deep learning, has been used in typical x-ray computed tomography (CT) applications, including image reconstruction, image enhancement, image domain feature detection and image domain feature characterization. To our know...
Humans Image Processing, Computer-Assisted Feasibility Studies Phantoms, Imaging Databases, Factual Tomography, X-Ray Computed Deep Learning
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A deep convolutional neural network using directional wavelets for low-dose X-ray CT reconstruction.

Medical physics Oct 1, 2017
PURPOSE: Due to the potential risk of inducing cancer, radiation exposure by X-ray CT devices should be reduced for routine patient scanning. However, in low-dose X-ray CT, severe artifacts typically occur due to photon starvation, beam hardening, an...
Image Processing, Computer-Assisted Artifacts Radiation Dosage Signal-To-Noise Ratio Humans Wavelet Analysis Neural Networks, Computer Tomography, X-Ray Computed
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Deep learning for polyp recognition in wireless capsule endoscopy images.

Medical physics Apr 1, 2017
PURPOSE: Wireless capsule endoscopy (WCE) enables physicians to examine the digestive tract without any surgical operations, at the cost of a large volume of images to be analyzed. In the computer-aided diagnosis of WCE images, the main challenge ari...
Machine Learning Image Processing, Computer-Assisted Capsule Endoscopy Polyps
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Improving CCTA-based lesions' hemodynamic significance assessment by accounting for partial volume modeling in automatic coronary lumen segmentation.

Medical physics Mar 1, 2017
PURPOSE: The goal of this study was to assess the potential added benefit of accounting for partial volume effects (PVE) in an automatic coronary lumen segmentation algorithm that is used to determine the hemodynamic significance of a coronary artery...
Humans Coronary Stenosis Machine Learning Coronary Angiography Pattern Recognition, Automated ROC Curve Software Computed Tomography Angiography Datasets as Topic Imaging, Three-Dimensional Retrospective Studies Models, Cardiovascular Coronary Vessels Area Under Curve Hemodynamics
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MRI-based prostate cancer detection with high-level representation and hierarchical classification.

Medical physics Mar 1, 2017
PURPOSE: Extracting the high-level feature representation by using deep neural networks for detection of prostate cancer, and then based on high-level feature representation constructing hierarchical classification to refine the detection results.
Magnetic Resonance Imaging Neural Networks, Computer Image Interpretation, Computer-Assisted Prostatic Neoplasms Sensitivity and Specificity Datasets as Topic Humans Male Prostate
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Discriminating solitary cysts from soft tissue lesions in mammography using a pretrained deep convolutional neural network.

Medical physics Mar 1, 2017
PURPOSE: It is estimated that 7% of women in the western world will develop palpable breast cysts in their lifetime. Even though cysts have been correlated with risk of developing breast cancer, many of them are benign and do not require follow-up. W...
ROC Curve Mammography Diagnosis, Differential Neural Networks, Computer Female Breast Neoplasms False Positive Reactions Breast Cyst Area Under Curve Radiographic Image Interpretation, Computer-Assisted Datasets as Topic Humans
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Segmentation of organs-at-risks in head and neck CT images using convolutional neural networks.

Medical physics Feb 1, 2017
PURPOSE: Accurate segmentation of organs-at-risks (OARs) is the key step for efficient planning of radiation therapy for head and neck (HaN) cancer treatment. In the work, we proposed the first deep learning-based algorithm, for segmentation of OARs ...
Radiotherapy Planning, Computer-Assisted Radiotherapy, Intensity-Modulated Humans Image Processing, Computer-Assisted Neural Networks, Computer Markov Chains Head and Neck Neoplasms Software Observer Variation Organs at Risk Tomography, X-Ray Computed
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Using deep learning to segment breast and fibroglandular tissue in MRI volumes.

Medical physics Feb 1, 2017
PURPOSE: Automated segmentation of breast and fibroglandular tissue (FGT) is required for various computer-aided applications of breast MRI. Traditional image analysis and computer vision techniques, such atlas, template matching, or, edge and surfac...
Datasets as Topic Pattern Recognition, Automated Atlases as Topic Breast Density Mammography Skin Artifacts Middle Aged Female Image Processing, Computer-Assisted Aged Magnetic Resonance Imaging Humans Adult Machine Learning
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Learning-based deformable image registration for infant MR images in the first year of life.

Medical physics Jan 1, 2017
PURPOSE: Many brain development studies have been devoted to investigate dynamic structural and functional changes in the first year of life. To quantitatively measure brain development in such a dynamic period, accurate image registration for differ...
Image Processing, Computer-Assisted Magnetic Resonance Imaging Brain Humans Infant, Newborn Machine Learning Infant
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