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2022-088 Neural Networks for Adipose Tissue Segmentation on Magnetic Resonance Images

Summary: UCLA researchers in the Department of Radiological Sciences have developed a novel neural network that can segment and quantify visceral and subcutaneous adipose tissues on magnetic resonance images automatically, accurately, and rapidly, to enhance clinical methods for characterizing and monitoring risk for cardiometabolic diseases in patients...

Published: 1/17/2024 | Inventor(s): Holden Wu, Sevgi Gokce Kafali-Tekat

Keywords(s): Advanced Computing / AI, Artificial Neural Network, Artificial Neural Network Artificial Neuron, cardiometabolic disease, Hypertension, Imaging, Machine Learning, Magnetic Resonance Imaging, Magnetic Resonance Imaging Pathology, Medical Imaging, MRI, multi-contrast imaging, obesity, Tissue (Biology), tissue annotation, tissue detection, tissue ribbons alignment, tissue segmentation

Category(s): Medical Devices > Medical Imaging, Platforms > Diagnostic Platform Technologies, Medical Devices > Medical Imaging > MRI, Therapeutics > Metabolism And Endocrinology