AIMC Topic: Imaging, Three-Dimensional

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Automatic lung nodule detection using a 3D deep convolutional neural network combined with a multi-scale prediction strategy in chest CTs.

Computers in biology and medicine Oct 12, 2018
OBJECTIVE: A novel computer-aided detection (CAD) scheme for lung nodule detection using a 3D deep convolutional neural network combined with a multi-scale prediction strategy is proposed to assist radiologists by providing a second opinion on accura...
Lung Neoplasms Cluster Analysis Solitary Pulmonary Nodule Radiographic Image Interpretation, Computer-Assisted Radiography, Thoracic Humans Neural Networks, Computer Tomography, X-Ray Computed Lung Imaging, Three-Dimensional
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3D convolutional neural networks for detection and severity staging of meniscus and PFJ cartilage morphological degenerative changes in osteoarthritis and anterior cruciate ligament subjects.

Journal of magnetic resonance imaging : JMRI Oct 10, 2018
BACKGROUND: Semiquantitative assessment of MRI plays a central role in musculoskeletal research; however, in the clinical setting MRI reports often tend to be subjective and qualitative. Grading schemes utilized in research are not used because they ...
Sensitivity and Specificity Anterior Cruciate Ligament Injuries Male Anterior Cruciate Ligament Retrospective Studies Middle Aged Anterior Cruciate Ligament Reconstruction Female Severity of Illness Index Meniscus Imaging, Three-Dimensional Reproducibility of Results ROC Curve Aged Magnetic Resonance Imaging Adult False Positive Reactions Osteoarthritis, Knee Humans Cartilage, Articular Neural Networks, Computer
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Automated segmentation of en face choroidal images obtained by optical coherent tomography by machine learning.

Japanese journal of ophthalmology Oct 6, 2018
PURPOSE: To develop an automated method to segment the choroidal layers of en face optical coherent tomography (OCT) images by machine learning.
Young Adult Adult Cross-Sectional Studies Male Middle Aged Female Reproducibility of Results Aged Aged, 80 and over Imaging, Three-Dimensional Choroid Tomography, Optical Coherence Healthy Volunteers Retinal Pigment Epithelium Humans Machine Learning Prospective Studies
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3-D Quantification of Filopodia in Motile Cancer Cells.

IEEE transactions on medical imaging Oct 5, 2018
We present a 3D bioimage analysis workflow to quantitatively analyze single, actin-stained cells with filopodial protrusions of diverse structural and temporal attributes, such as number, length, thickness, level of branching, and lifetime, in time-l...
Imaging, Three-Dimensional Image Processing, Computer-Assisted Lung Neoplasms Adenocarcinoma Cell Line Pseudopodia Humans Neural Networks, Computer Algorithms Neoplasms Spatio-Temporal Analysis
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Prediction of spinal curve progression in Adolescent Idiopathic Scoliosis using Random Forest regression.

Computers in biology and medicine Oct 4, 2018
BACKGROUND: The progression of the spinal curve represents one of the major concerns in the assessment of Adolescent Idiopathic Scoliosis (AIS). The prediction of the shape of the spine from the first visit could guide the management of AIS and provi...
Humans Machine Learning Databases, Factual Disease Progression Adolescent Imaging, Three-Dimensional Thoracic Vertebrae Regression Analysis Decision Trees Radiography Scoliosis
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Comparison of radiomics machine-learning classifiers and feature selection for differentiation of sacral chordoma and sacral giant cell tumour based on 3D computed tomography features.

European radiology Oct 2, 2018
OBJECTIVE: We aimed to identify optimal machine-learning methods for preoperative differentiation of sacral chordoma (SC) and sacral giant cell tumour (SGCT) based on 3D non-enhanced computed tomography (CT) and CT-enhanced (CTE) features.
Humans Machine Learning Retrospective Studies Tomography, X-Ray Computed Female Spinal Neoplasms Giant Cell Tumors Sacrum Adult Imaging, Three-Dimensional Chordoma Male Support Vector Machine Middle Aged Diagnosis, Differential ROC Curve Image Interpretation, Computer-Assisted Linear Models
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3D Deep Learning from CT Scans Predicts Tumor Invasiveness of Subcentimeter Pulmonary Adenocarcinomas.

Cancer research Oct 2, 2018
: Identification of early-stage pulmonary adenocarcinomas before surgery, especially in cases of subcentimeter cancers, would be clinically important and could provide guidance to clinical decision making. In this study, we developed a deep learning ...
Humans Young Adult Adenocarcinoma Deep Learning Neoplasm Invasiveness Adult Neural Networks, Computer Adolescent Decision Making Male Precision Medicine Imaging, Three-Dimensional Area Under Curve Middle Aged Prognosis Image Processing, Computer-Assisted Mutation Diagnosis, Computer-Assisted Retrospective Studies Lung Neoplasms Tomography, X-Ray Computed Female Aged Aged, 80 and over
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Flexible Learning-Free Segmentation and Reconstruction of Neural Volumes.

Scientific reports Sep 24, 2018
Imaging is a dominant strategy for data collection in neuroscience, yielding stacks of images that often scale to gigabytes of data for a single experiment. Machine learning algorithms from computer vision can serve as a pair of virtual eyes that tir...
Image Processing, Computer-Assisted Machine Learning Mice Algorithms X-Ray Microtomography Synchrotrons Animals Imaging, Three-Dimensional
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Evaluation of Commonly Used Algorithms for Thyroid Ultrasound Images Segmentation and Improvement Using Machine Learning Approaches.

Journal of healthcare engineering Sep 23, 2018
The thyroid is one of the largest endocrine glands in the human body, which is involved in several body mechanisms like controlling protein synthesis and the body's sensitivity to other hormones and use of energy sources. Hence, it is of prime import...
Ultrasonography Algorithms Humans Imaging, Three-Dimensional Automation Reproducibility of Results Image Processing, Computer-Assisted Neural Networks, Computer Diagnosis, Computer-Assisted Decision Trees Machine Learning Thyroid Gland Software Thyroid Neoplasms
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Comparison between support vector machine and deep learning, machine-learning technologies for detecting epiretinal membrane using 3D-OCT.

International ophthalmology Sep 14, 2018
PURPOSE: In this study, we compared deep learning (DL) with support vector machine (SVM), both of which use three-dimensional optical coherence tomography (3D-OCT) images for detecting epiretinal membrane (ERM).
Humans Machine Learning Female Aged Male Tomography, Optical Coherence Retina Early Diagnosis Epiretinal Membrane Deep Learning Imaging, Three-Dimensional Support Vector Machine Visual Acuity
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