AIMC Topic:
Image Interpretation, Computer-Assisted
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Hyperspectral Tissue Image Segmentation Using Semi-Supervised NMF and Hierarchical Clustering.
IEEE transactions on medical imaging
Nov 26, 2018
Hyperspectral imaging (HSI) of tissue samples in the mid-infrared (mid-IR) range provides spectro-chemical and tissue structure information at sub-cellular spatial resolution. Disease states can be directly assessed by analyzing the mid-IR spectra of...
Supervised machine learning quality control for magnetic resonance artifacts in neonatal data sets.
Human brain mapping
Nov 22, 2018
Quality control (QC) of brain magnetic resonance images (MRI) is an important process requiring a significant amount of manual inspection. Major artifacts, such as severe subject motion, are easy to identify to naïve observers but lack automated iden...
Novel Effective Connectivity Inference Using Ultra-Group Constrained Orthogonal Forward Regression and Elastic Multilayer Perceptron Classifier for MCI Identification.
IEEE transactions on medical imaging
Nov 20, 2018
Mild cognitive impairment (MCI) detection is important, such that appropriate interventions can be imposed to delay or prevent its progression to severe stages, including Alzheimer's disease (AD). Brain connectivity network inferred from the function...
Real-Time Standard View Classification in Transthoracic Echocardiography Using Convolutional Neural Networks.
Ultrasound in medicine & biology
Nov 20, 2018
Transthoracic echocardiography examinations are usually performed according to a protocol comprising different probe postures providing standard views of the heart. These are used as a basis when assessing cardiac function, and it is essential that t...
Automated segmentation of knee bone and cartilage combining statistical shape knowledge and convolutional neural networks: Data from the Osteoarthritis Initiative.
Medical image analysis
Nov 17, 2018
We present a method for the automated segmentation of knee bones and cartilage from magnetic resonance imaging (MRI) that combines a priori knowledge of anatomical shape with Convolutional Neural Networks (CNNs). The proposed approach incorporates 3D...
Retinal image analytics detects white matter hyperintensities in healthy adults.
Annals of clinical and translational neurology
Nov 15, 2018
OBJECTIVE: We investigated whether an automatic retinal image analysis (ARIA) incorporating machine learning approach can identify asymptomatic older adults harboring high burden of white matter hyperintensities (WMH) using MRI as gold standard.
Multi-sequence myocardium segmentation with cross-constrained shape and neural network-based initialization.
Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society
Nov 15, 2018
For myocardial infarction (MI) patients, delayed enhancement (DE) and T2-weighted cardiovascular magnetic resonance imaging (CMR) can play significant roles in diagnosis, prognosis and therapeutic strategy evaluation. However, the non-rigid registrat...
Image Processing-Based Recognition of Wall Defects Using Machine Learning Approaches and Steerable Filters.
Computational intelligence and neuroscience
Nov 15, 2018
Detection of defects including cracks and spalls on wall surface in high-rise buildings is a crucial task of buildings' maintenance. If left undetected and untreated, these defects can significantly affect the structural integrity and the aesthetic a...
Synthesizing Chest X-Ray Pathology for Training Deep Convolutional Neural Networks.
IEEE transactions on medical imaging
Nov 14, 2018
Medical datasets are often highly imbalanced with over-representation of prevalent conditions and poor representation of rare medical conditions. Due to privacy concerns, it is challenging to aggregate large datasets between health care institutions....
Splenomegaly Segmentation on Multi-Modal MRI Using Deep Convolutional Networks.
IEEE transactions on medical imaging
Nov 13, 2018
The findings of splenomegaly, abnormal enlargement of the spleen, is a non-invasive clinical biomarker for liver and spleen diseases. Automated segmentation methods are essential to efficiently quantify splenomegaly from clinically acquired abdominal...
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