AIMC Journal:
Medical image analysis

Showing 631 to 640 of 699 articles

Deep learning analysis of the myocardium in coronary CT angiography for identification of patients with functionally significant coronary artery stenosis.

Medical image analysis Nov 26, 2017
In patients with coronary artery stenoses of intermediate severity, the functional significance needs to be determined. Fractional flow reserve (FFR) measurement, performed during invasive coronary angiography (ICA), is most often used in clinical pr...
Humans Coronary Stenosis Cardiac-Gated Imaging Techniques Deep Learning Contrast Media Coronary Angiography Heart Ventricles Iohexol Computed Tomography Angiography Sensitivity and Specificity Male Middle Aged Algorithms Female Reproducibility of Results
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Learning normalized inputs for iterative estimation in medical image segmentation.

Medical image analysis Nov 14, 2017
In this paper, we introduce a simple, yet powerful pipeline for medical image segmentation that combines Fully Convolutional Networks (FCNs) with Fully Convolutional Residual Networks (FC-ResNets). We propose and examine a design that takes particula...
Image Processing, Computer-Assisted Magnetic Resonance Imaging Male Lumbar Vertebrae Liver Neoplasms Prostatic Diseases Imaging, Three-Dimensional Humans Neural Networks, Computer Algorithms Tomography, X-Ray Computed
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Learning and combining image neighborhoods using random forests for neonatal brain disease classification.

Medical image analysis Aug 9, 2017
It is challenging to characterize and classify normal and abnormal brain development during early childhood. To reduce the complexity of heterogeneous data population, manifold learning techniques are increasingly applied, which find a low-dimensiona...
Humans Infant, Newborn Magnetic Resonance Imaging Infant, Newborn, Diseases Cerebral Ventricles Fetal Growth Retardation Supervised Machine Learning Image Interpretation, Computer-Assisted Brain Diseases
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Ensemble of expert deep neural networks for spatio-temporal denoising of contrast-enhanced MRI sequences.

Medical image analysis Aug 2, 2017
Dynamic contrast-enhanced MRI (DCE-MRI) is an imaging protocol where MRI scans are acquired repetitively throughout the injection of a contrast agent. The analysis of dynamic scans is widely used for the detection and quantification of blood-brain ba...
Reproducibility of Results Sensitivity and Specificity Blood-Brain Barrier Humans Neural Networks, Computer Algorithms Image Enhancement Anisotropy Contrast Media Magnetic Resonance Imaging Brain Mapping
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Efficient and robust cell detection: A structured regression approach.

Medical image analysis Jul 26, 2017
Efficient and robust cell detection serves as a critical prerequisite for many subsequent biomedical image analysis methods and computer-aided diagnosis (CAD). It remains a challenging task due to touching cells, inhomogeneous background noise, and l...
Humans Neural Networks, Computer Algorithms Deep Learning Breast Neoplasms Female Cytological Techniques Reproducibility of Results Neuroendocrine Tumors Bone Marrow Cells Uterine Cervical Neoplasms Sensitivity and Specificity Diagnosis, Computer-Assisted
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A survey on deep learning in medical image analysis.

Medical image analysis Jul 26, 2017
Deep learning algorithms, in particular convolutional networks, have rapidly become a methodology of choice for analyzing medical images. This paper reviews the major deep learning concepts pertinent to medical image analysis and summarizes over 300 ...
Algorithms Image Processing, Computer-Assisted Neural Networks, Computer Diagnostic Imaging Machine Learning Humans
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Central focused convolutional neural networks: Developing a data-driven model for lung nodule segmentation.

Medical image analysis Jun 30, 2017
Accurate lung nodule segmentation from computed tomography (CT) images is of great importance for image-driven lung cancer analysis. However, the heterogeneity of lung nodules and the presence of similar visual characteristics between nodules and the...
Sensitivity and Specificity Diagnosis, Computer-Assisted Lung Neoplasms Neural Networks, Computer Machine Learning Tomography, X-Ray Computed Humans
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Multi-modal classification of neurodegenerative disease by progressive graph-based transductive learning.

Medical image analysis May 13, 2017
Graph-based transductive learning (GTL) is a powerful machine learning technique that is used when sufficient training data is not available. In particular, conventional GTL approaches first construct a fixed inter-subject relation graph that is base...
Male Humans Reproducibility of Results Supervised Machine Learning Aged Disease Progression Alzheimer Disease Machine Learning Parkinson Disease Female Sensitivity and Specificity
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A structured latent model for ovarian carcinoma subtyping from histopathology slides.

Medical image analysis May 9, 2017
Accurate subtyping of ovarian carcinomas is an increasingly critical and often challenging diagnostic process. This work focuses on the development of an automatic classification model for ovarian carcinoma subtyping. Specifically, we present a novel...
Pattern Recognition, Automated Histological Techniques Sensitivity and Specificity Support Vector Machine Algorithms Ovarian Neoplasms Humans Female Reproducibility of Results
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3D deeply supervised network for automated segmentation of volumetric medical images.

Medical image analysis May 8, 2017
While deep convolutional neural networks (CNNs) have achieved remarkable success in 2D medical image segmentation, it is still a difficult task for CNNs to segment important organs or structures from 3D medical images owing to several mutually affect...
Imaging, Three-Dimensional Supervised Machine Learning Neural Networks, Computer Reproducibility of Results Sensitivity and Specificity Humans
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