title: Automated assessment of thigh composition using machine learning for Dixon magnetic resonance images.
abstract: OBJECTIVE
  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.
  
  
  METHODS
  The automatic detection of body composition is formulized as a three-class classification issue. Each image voxel in the training dataset is assigned with a correct label. A voxel classifier is trained and subsequently used to predict unseen data. Morphological operations are finally applied to generate volumetric segmented images for different structures. We applied this algorithm on datasets of (1) four contrast images, (2) water and fat images, and (3) unsuppressed images acquired from 190 subjects.
  
  
  RESULTS
  The proposed method using four contrasts achieved most accurate and robust segmentation compared to the use of combined fat and water images and the use of unsuppressed image, average Dice coefficients of 0.94 ± 0.03, 0.96 ± 0.03, 0.80 ± 0.03, and 0.97 ± 0.01 has been achieved to bone region, subcutaneous adipose tissue (SAT), inter-muscular adipose tissue (IMAT), and muscle respectively.
  
  
  CONCLUSIONS
  Our proposed method based on machine learning produces accurate tissue quantification and showed an effective use of large information provided by the four contrast images from Dixon MRI.
authors.affiliation: Institute of Geriatrics and Active Ageing, Tan Tock Seng Hospital, Singapore, Singapore. yuxin_yang@ttsh.com.sg.
authors.forename: Yu Xin
authors.initials: YX
authors.lastname: Yang
authors.affiliation: Institute of Geriatrics and Active Ageing, Tan Tock Seng Hospital, Singapore, Singapore.|Department of Geriatric Medicine, Tan Tock Seng Hospital, Singapore, Singapore.
authors.forename: Mei Sian
authors.initials: MS
authors.lastname: Chong
authors.affiliation: Institute of Geriatrics and Active Ageing, Tan Tock Seng Hospital, Singapore, Singapore.|Department of Geriatric Medicine, Tan Tock Seng Hospital, Singapore, Singapore.
authors.forename: Laura
authors.initials: L
authors.lastname: Tay
authors.affiliation: Institute of Geriatrics and Active Ageing, Tan Tock Seng Hospital, Singapore, Singapore.
authors.forename: Suzanne
authors.initials: S
authors.lastname: Yew
authors.affiliation: Institute of Geriatrics and Active Ageing, Tan Tock Seng Hospital, Singapore, Singapore.
authors.forename: Audrey
authors.initials: A
authors.lastname: Yeo
authors.affiliation: Department of Diagnostic Radiology, Tan Tock Seng Hospital, Singapore, Singapore.
authors.forename: Cher Heng
authors.initials: CH
authors.lastname: Tan
mesh_terms: D000368:Aged; D000369:Aged, 80 and over; D000465:Algorithms; D001823:Body Composition; D005260:Female; D006801:Humans; D007090:Image Interpretation, Computer-Assisted / Q000379:methods; D007091:Image Processing, Computer-Assisted / Q000379:methods; D021621:Imaging, Three-Dimensional / Q000379:methods; D000069550:Machine Learning; D008279:Magnetic Resonance Imaging / Q000379:methods*; D008297:Male; D008875:Middle Aged; D010363:Pattern Recognition, Automated / Q000379:methods*; D050151:Subcutaneous Fat / Q000000981:diagnostic imaging*; D013848:Thigh
keywords: Body composition; Dixon sequence; Image segmentation; MRI; Machine learning