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Deep Learning-based Automated Aortic Area and Distensibility Assessment: The Multi-Ethnic Study of Atherosclerosis (MESA)
CCC for comparison of manual and CNN maximum and minimum ascending aortic areas were 0. 97 and 0. 95, respectively, on the internal test set and 0. 997 and 0. 995, respectively, for the external test.
Multiparametric Deep Learning Tissue Signatures for a Radiological Biomarker of Breast Cancer: Preliminary Results
For example, using a deep learning network, we developed and tested a multiparametric deep learning (MPDL) network for segmentation and classification using multiparametric magnetic resonance imaging (mpMRI) radiological images.
Ω-Net (Omega-Net): Fully Automatic, Multi-View Cardiac MR Detection, Orientation, and Segmentation with Deep Neural Networks
Pixelwise segmentation of the left ventricular (LV) myocardium and the four cardiac chambers in 2-D steady state free precession (SSFP) cine sequences is an essential preprocessing step for a wide range of analyses.
Feature Tracking Cardiac Magnetic Resonance via Deep Learning and Spline Optimization
Feature tracking Cardiac Magnetic Resonance (CMR) has recently emerged as an area of interest for quantification of regional cardiac function from balanced, steady state free precession (SSFP) cine sequences.
Explaining Radiological Emphysema Subtypes with Unsupervised Texture Prototypes: MESA COPD Study
Pulmonary emphysema is traditionally subcategorized into three subtypes, which have distinct radiological appearances on computed tomography (CT) and can help with the diagnosis of chronic obstructive pulmonary disease (COPD).
