AIMC Journal:
Medical physics

Showing 661 to 670 of 759 articles

A deep 3D residual CNN for false-positive reduction in pulmonary nodule detection.

Medical physics Mar 25, 2018
PURPOSE: The automatic detection of pulmonary nodules using CT scans improves the efficiency of lung cancer diagnosis, and false-positive reduction plays a significant role in the detection. In this paper, we focus on the false-positive reduction tas...
False Positive Reactions Imaging, Three-Dimensional Lung Neoplasms Humans Neural Networks, Computer Tomography, X-Ray Computed
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Deep feature classification of angiomyolipoma without visible fat and renal cell carcinoma in abdominal contrast-enhanced CT images with texture image patches and hand-crafted feature concatenation.

Medical physics Mar 25, 2018
PURPOSE: To develop an automatic deep feature classification (DFC) method for distinguishing benign angiomyolipoma without visible fat (AMLwvf) from malignant clear cell renal cell carcinoma (ccRCC) from abdominal contrast-enhanced computer tomograph...
Contrast Media Diagnosis, Differential Sensitivity and Specificity Image Processing, Computer-Assisted Radiography, Abdominal Angiomyolipoma Kidney Neoplasms Carcinoma, Renal Cell Humans Machine Learning Tomography, X-Ray Computed
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A machine learning approach to the accurate prediction of monitor units for a compact proton machine.

Medical physics Mar 23, 2018
PURPOSE: Clinical treatment planning systems for proton therapy currently do not calculate monitor units (MUs) in passive scatter proton therapy due to the complexity of the beam delivery systems. Physical phantom measurements are commonly employed t...
Proton Therapy Scattering, Radiation Phantoms, Imaging Radiotherapy Planning, Computer-Assisted Humans Machine Learning
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Interleaved 3D-CNNs for joint segmentation of small-volume structures in head and neck CT images.

Medical physics Mar 23, 2018
PURPOSE: Accurate 3D image segmentation is a crucial step in radiation therapy planning of head and neck tumors. These segmentation results are currently obtained by manual outlining of tissues, which is a tedious and time-consuming procedure. Automa...
Humans Imaging, Three-Dimensional Head and Neck Neoplasms Neural Networks, Computer Tomography, X-Ray Computed Joints
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Artificial intelligence will soon change the landscape of medical physics research and practice.

Medical physics Mar 13, 2018
Medicine Physics Artificial Intelligence Research
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Deep learning for segmentation of brain tumors: Impact of cross-institutional training and testing.

Medical physics Feb 8, 2018
BACKGROUND AND PURPOSE: Convolutional neural networks (CNNs) are commonly used for segmentation of brain tumors. In this work, we assess the effect of cross-institutional training on the performance of CNNs.
Image Processing, Computer-Assisted Magnetic Resonance Imaging Machine Learning Brain Neoplasms Humans Neural Networks, Computer
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Feasibility of predicting tumor motion using online data acquired during treatment and a generalized neural network optimized with offline patient tumor trajectories.

Medical physics Jan 12, 2018
PURPOSE: The accurate prediction of intrafraction lung tumor motion is required to compensate for system latency in image-guided adaptive radiotherapy systems. The goal of this study was to identify an optimal prediction model that has a short learni...
Neural Networks, Computer Neoplasms Feasibility Studies Radiotherapy, Image-Guided
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A deep learning method for classifying mammographic breast density categories.

Medical physics Dec 22, 2017
PURPOSE: Mammographic breast density is an established risk marker for breast cancer and is visually assessed by radiologists in routine mammogram image reading, using four qualitative Breast Imaging and Reporting Data System (BI-RADS) breast density...
Mammography Area Under Curve Image Interpretation, Computer-Assisted Humans Female Neural Networks, Computer Machine Learning ROC Curve Retrospective Studies
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Principal component reconstruction (PCR) for cine CBCT with motion learning from 2D fluoroscopy.

Medical physics Dec 11, 2017
PURPOSE: This work aims to generate cine CT images (i.e., 4D images with high-temporal resolution) based on a novel principal component reconstruction (PCR) technique with motion learning from 2D fluoroscopic training images.
Movement Cone-Beam Computed Tomography Four-Dimensional Computed Tomography Image Processing, Computer-Assisted Machine Learning Fluoroscopy Artifacts
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Deep reinforcement learning for automated radiation adaptation in lung cancer.

Medical physics Nov 14, 2017
PURPOSE: To investigate deep reinforcement learning (DRL) based on historical treatment plans for developing automated radiation adaptation protocols for nonsmall cell lung cancer (NSCLC) patients that aim to maximize tumor local control at reduced r...
Lung Neoplasms Neural Networks, Computer Automation
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