Automated assessment of thigh composition using machine learning for Dixon magnetic resonance images.

Journal: Magma (New York, N.Y.)

Published Date: Oct 1, 2016

Abstract

OBJECTIVES: To develop and validate a machine learning based automated segmentation method that jointly analyzes the four contrasts provided by Dixon MRI technique for improved thigh composition segmentation accuracy.

Authors

Yu Xin Yang

Institute of Geriatrics and Active Ageing, Tan Tock Seng Hospital, Singapore, Singapore. yuxin_yang@ttsh.com.sg.
Mei Sian Chong

Institute of Geriatrics and Active Ageing, Tan Tock Seng Hospital, Singapore, Singapore.
Laura Tay

Institute of Geriatrics and Active Ageing, Tan Tock Seng Hospital, Singapore, Singapore.
Suzanne Yew

Institute of Geriatrics and Active Ageing, Tan Tock Seng Hospital, Singapore, Singapore.
Audrey Yeo

Institute of Geriatrics and Active Ageing, Tan Tock Seng Hospital, Singapore, Singapore.
Cher Heng Tan

Department of Diagnostic Radiology, Tan Tock Seng Hospital, Singapore, Singapore.

Keywords

Aged Aged, 80 and over Algorithms Body Composition Female Humans Image Interpretation, Computer-Assisted Image Processing, Computer-Assisted Imaging, Three-Dimensional Machine Learning Magnetic Resonance Imaging Male Middle Aged Pattern Recognition, Automated Subcutaneous Fat Thigh

External Resources

View on PubMed Access via DOI PubMed (27026244)

Automated assessment of thigh composition using machine learning for Dixon magnetic resonance images.

Abstract

Authors

Keywords

External Resources

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