Automated Segmentation of Visceral, Deep Subcutaneous, and Superficial Subcutaneous Adipose Tissue Volumes in MRI of Neonates and Young Children.
Yeshe Manuel KwayKashthuri ThirumuruganMya-Thway TintNavin MichaelLynette Pei-Chi ShekFabian Kok Peng YapKok Hian TanKeith M GodfreyYap Seng ChongMarielle Valerie FortierUte C MarxJohan G ErikssonYung Seng LeeSambasivam Sendhil VelanMengling FengSuresh Anand SadananthanPublished in: Radiology. Artificial intelligence (2021)
The proposed segmentation approach provided accurate automated volumetric assessment of AAT compartments on MR images of neonates and children.Keywords Pediatrics, Deep Learning, Convolutional Neural Networks, Water-Fat MRI, Image Segmentation, Deep and Superficial Subcutaneous Adipose Tissue, Visceral Adipose TissueClinical trial registration no. NCT01174875 Supplemental material is available for this article. © RSNA, 2021.
Keyphrases
- deep learning
- convolutional neural network
- adipose tissue
- insulin resistance
- contrast enhanced
- magnetic resonance imaging
- artificial intelligence
- high fat diet
- machine learning
- low birth weight
- diffusion weighted imaging
- magnetic resonance
- study protocol
- computed tomography
- clinical trial
- young adults
- randomized controlled trial
- metabolic syndrome
- skeletal muscle
- phase iii
- type diabetes
- preterm infants
- double blind
- mass spectrometry