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

• Published in: Skeletal radiology Vol. 49; no. 3; pp. 387 - 395
• Main Authors: Hemke, Robert, Buckless, Colleen G., Tsao, Andrew, Wang, Benjamin, Torriani, Martin
• Format: Journal Article
• Language: English
• Published: Berlin/Heidelberg Springer Berlin Heidelberg 01.03.2020; Springer; Springer Nature B.V
• Subjects: Acetabulum; Adipose tissue; Adipose Tissue - diagnostic imaging; Adipose tissues; Artificial neural networks; Automation; Body Composition; Bone composition; Computed tomography; Contrast Media; CT imaging; Data augmentation; Datasets; Datasets as Topic; Deep learning; Equalization; Female; Histograms; Humans; Image processing; Image segmentation; Imaging; Male; Medical imaging equipment; Medicine; Medicine & Public Health; Middle Aged; Muscle, Skeletal - diagnostic imaging; Muscles; Neural networks; Neural Networks, Computer; Nuclear Medicine; Orthopedics; Pathology; Pelvis; Pelvis - diagnostic imaging; Physiological aspects; Radiology; Retrospective Studies; Scientific Article; Tomography, X-Ray Computed; Training; Deep learning; Muscle; Segmentation; Computed tomography; Pelvis; Body composition

Objective 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. Materials and m...