AIMC Topic: Radiographic Image Interpretation, Computer-Assisted

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Machine-learning phenotypic classification of bicuspid aortopathy.

The Journal of thoracic and cardiovascular surgery Sep 14, 2017
BACKGROUND: Bicuspid aortic valves (BAV) are associated with incompletely characterized aortopathy. Our objectives were to identify distinct patterns of aortopathy using machine-learning methods and characterize their association with valve morpholog...
Aorta, Thoracic Computed Tomography Angiography Aortic Aneurysm Aortography Sinus of Valsalva Aortic Valve Insufficiency Bicuspid Aortic Valve Disease Heart Valve Diseases Pattern Recognition, Automated Adult Cross-Sectional Studies Male Middle Aged Diagnosis, Computer-Assisted Machine Learning Aortic Valve Stenosis Phenotype Aortic Valve Female Predictive Value of Tests Reproducibility of Results Radiographic Image Interpretation, Computer-Assisted Humans Aged
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Automated Analysis of Unregistered Multi-View Mammograms With Deep Learning.

IEEE transactions on medical imaging Sep 12, 2017
We describe an automated methodology for the analysis of unregistered cranio-caudal (CC) and medio-lateral oblique (MLO) mammography views in order to estimate the patient's risk of developing breast cancer. The main innovation behind this methodolog...
ROC Curve Imaging, Three-Dimensional Mammography Radiographic Image Interpretation, Computer-Assisted Breast Humans Machine Learning Databases, Factual Female Breast Neoplasms
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Please Don't Move-Evaluating Motion Artifact From Peripheral Quantitative Computed Tomography Scans Using Textural Features.

Journal of clinical densitometry : the official journal of the International Society for Clinical Densitometry Aug 8, 2017
Most imaging methods, including peripheral quantitative computed tomography (pQCT), are susceptible to motion artifacts particularly in fidgety pediatric populations. Methods currently used to address motion artifact include manual screening (visual ...
Humans Radiographic Image Interpretation, Computer-Assisted Artifacts Tibia Radius Patient Positioning Machine Learning Tomography, X-Ray Computed Reproducibility of Results Decision Trees Movement Epiphyses Diaphyses
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An Automatic Detection System of Lung Nodule Based on Multigroup Patch-Based Deep Learning Network.

IEEE journal of biomedical and health informatics Jul 14, 2017
High-efficiency lung nodule detection dramatically contributes to the risk assessment of lung cancer. It is a significant and challenging task to quickly locate the exact positions of lung nodules. Extensive work has been done by researchers around t...
Lung Deep Learning Lung Neoplasms Radiographic Image Interpretation, Computer-Assisted Humans Algorithms Tomography, X-Ray Computed
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Fully automatic detection of lung nodules in CT images using a hybrid feature set.

Medical physics Jun 16, 2017
PURPOSE: The aim of this study was to develop a novel technique for lung nodule detection using an optimized feature set. This feature set has been achieved after rigorous experimentation, which has helped in reducing the false positives significantl...
Radiographic Image Interpretation, Computer-Assisted Humans Algorithms Tomography, X-Ray Computed Sensitivity and Specificity Diagnosis, Computer-Assisted Lung Neoplasms Support Vector Machine Solitary Pulmonary Nodule
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Impact of knowledge-based iterative model reconstruction on myocardial late iodine enhancement in computed tomography and comparison with cardiac magnetic resonance.

The international journal of cardiovascular imaging Apr 13, 2017
We evaluated the image quality and diagnostic performance of late iodine enhancement computed tomography (LIE-CT) with knowledge-based iterative model reconstruction (IMR) for the detection of myocardial infarction (MI) in comparison with late gadoli...
Iopamidol Middle Aged Coronary Circulation Electrocardiography Retrospective Studies Knowledge Bases Contrast Media Myocardial Perfusion Imaging Female Myocardium Observer Variation Gadolinium DTPA Predictive Value of Tests Humans Reproducibility of Results Multidetector Computed Tomography Coronary Angiography Magnetic Resonance Imaging, Cine Aged Computed Tomography Angiography Radiographic Image Interpretation, Computer-Assisted Cardiac-Gated Imaging Techniques Male Coronary Artery Disease Myocardial Infarction
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Automatic segmentation of liver tumors from multiphase contrast-enhanced CT images based on FCNs.

Artificial intelligence in medicine Mar 27, 2017
This paper presents a novel, fully automatic approach based on a fully convolutional network (FCN) for segmenting liver tumors from CT images. Specifically, we designed a multi-channel fully convolutional network (MC-FCN) to segment liver tumors from...
Contrast Media Predictive Value of Tests Radiographic Image Interpretation, Computer-Assisted Automation Databases, Factual Humans Retrospective Studies Artificial Intelligence Tomography, X-Ray Computed Reproducibility of Results Liver Neoplasms
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Early prediction of radiotherapy-induced parotid shrinkage and toxicity based on CT radiomics and fuzzy classification.

Artificial intelligence in medicine Mar 18, 2017
MOTIVATION: Patients under radiotherapy for head-and-neck cancer often suffer of long-term xerostomia, and/or consistent shrinkage of parotid glands. In order to avoid these drawbacks, adaptive therapy can be planned for patients at risk, if the pred...
Head and Neck Neoplasms Fuzzy Logic Predictive Value of Tests Bayes Theorem Radiographic Image Interpretation, Computer-Assisted Parotid Gland Early Diagnosis Radiotherapy Dosage Radiation Injuries Humans Machine Learning Tomography, X-Ray Computed Risk Factors Time Factors Radiation Exposure Cranial Irradiation Xerostomia
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Improved image quality with simultaneously reduced radiation exposure: Knowledge-based iterative model reconstruction algorithms for coronary CT angiography in a clinical setting.

Journal of cardiovascular computed tomography Feb 23, 2017
BACKGROUND: The aim of this study was to assess the potential for radiation dose reduction using knowledge-based iterative model reconstruction (K-IMR) algorithms in combination with ultra-low dose body mass index (BMI)-adapted protocols in coronary ...
Germany Coronary Vessels Radiation Dosage Signal-To-Noise Ratio Multidetector Computed Tomography Humans Coronary Angiography Algorithms Radiographic Image Interpretation, Computer-Assisted Computed Tomography Angiography Female Coronary Artery Disease Radiation Exposure Reproducibility of Results Knowledge Bases Aged Male Middle Aged Body Mass Index Predictive Value of Tests
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A deep learning approach for the analysis of masses in mammograms with minimal user intervention.

Medical image analysis Jan 28, 2017
We present an integrated methodology for detecting, segmenting and classifying breast masses from mammograms with minimal user intervention. This is a long standing problem due to low signal-to-noise ratio in the visualisation of breast masses, combi...
Breast Neoplasms Humans Mammography Machine Learning Bayes Theorem Female Radiographic Image Interpretation, Computer-Assisted Sensitivity and Specificity Breast
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