Clinical evaluation of fully automated thigh muscle and adipose tissue segmentation using a U-Net deep learning architecture in context of osteoarthritic knee pain

• Published in: Magma (New York, N.Y.) Vol. 33; no. 4; pp. 483 - 493
• Main Authors: Kemnitz, Jana, Baumgartner, Christian F., Eckstein, Felix, Chaudhari, Akshay, Ruhdorfer, Anja, Wirth, Wolfgang, Eder, Sebastian K., Konukoglu, Ender
• Format: Journal Article
• Language: English
• Published: Cham Springer International Publishing 01.08.2020
• Subjects: Biomedical Engineering and Bioengineering; Clinical Applications – Musculoskeletal; Computer Appl. in Life Sciences; Health Informatics; Imaging; Medicine; Medicine & Public Health; Radiology; Research Article; Solid State Physics; Deep learning; Muscle; Magnetic resonance imaging; Convolutional neural networks; Automated segmentation

Objective Segmentation of thigh muscle and adipose tissue is important for the understanding of musculoskeletal diseases such as osteoarthritis. Therefore, the purpose of this work is (a) to evaluate whether a fully automated approach provides accurate segmentation of muscles and adipose tissue cros...