Deep learning for automated segmentation of pelvic muscles, fat, and bone from CT studies for body composition assessment | Zendy

Robert Hemke | Zendy; Colleen Buckless | Zendy; Andrew Tsao | Zendy; Benjamin Wang | Zendy; Martin Torriani | Zendy

Open Access

Deep learning for automated segmentation of pelvic muscles, fat, and bone from CT studies for body composition assessment

Author(s) -

Robert Hemke,

Colleen Buckless,

Andrew Tsao,

Benjamin Wang,

Martin Torriani

Publication year - 2019

Publication title -

skeletal radiology

Language(s) - English

Resource type - Journals

SCImago Journal Rank - 0.571

H-Index - 91

eISSN - 1432-2161

pISSN - 0364-2348

DOI - 10.1007/s00256-019-03289-8

Subject(s) - segmentation , medicine , convolutional neural network , artificial intelligence , pelvis , deep learning , computer science , sørensen–dice coefficient , pattern recognition (psychology) , nuclear medicine , image segmentation , radiology

To develop a deep convolutional neural network (CNN) to automatically segment an axial CT image of the pelvis for body composition measures. We hypothesized that a deep CNN approach would achieve high accuracy when compared to manual segmentations as the reference standard.

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