Deep Learning Automated Segmentation for Muscle and Adipose Tissue from Abdominal Computed Tomography in Polytrauma Patients

Manual segmentation of muscle and adipose compartments from computed tomography (CT) axial images is a potential bottleneck in early rapid detection and quantification of sarcopenia. A prototype deep learning neural network was trained on a multi-center collection of 3413 abdominal cancer surgery su...

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Bibliographic Details
Published in:Sensors (Basel, Switzerland) Switzerland), 2021-03, Vol.21 (6), p.2083
Main Authors: Ackermans, Leanne L G C, Volmer, Leroy, Wee, Leonard, Brecheisen, Ralph, Sánchez-González, Patricia, Seiffert, Alexander P, Gómez, Enrique J, Dekker, Andre, Ten Bosch, Jan A, Olde Damink, Steven M W, Blokhuis, Taco J
Format: Article
Language:English
Subjects:
Abdomen
Adipose tissue
Adipose Tissue - diagnostic imaging
Algorithms
Attenuation
automated segmentation
Automation
Body composition
Body fat
Cancer surgery
Computed tomography
Correlation coefficients
Cross-Sectional Studies
Datasets
Deep Learning
deep learning neural network
Humans
Image quality
Medical imaging
Metastasis
Multiple Trauma - diagnostic imaging
Muscle, Skeletal - diagnostic imaging
Muscles
Musculoskeletal system
Neural networks
Older people
Patients
Sarcopenia
Tomography, X-Ray Computed
Trauma
Vertebrae
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Summary:Manual segmentation of muscle and adipose compartments from computed tomography (CT) axial images is a potential bottleneck in early rapid detection and quantification of sarcopenia. A prototype deep learning neural network was trained on a multi-center collection of 3413 abdominal cancer surgery subjects to automatically segment truncal muscle, subcutaneous adipose tissue and visceral adipose tissue at the L3 lumbar vertebral level. Segmentations were externally tested on 233 polytrauma subjects. Although after severe trauma abdominal CT scans are quickly and robustly delivered, with often motion or scatter artefacts, incomplete vertebral bodies or arms that influence image quality, the concordance was generally very good for the body composition indices of Skeletal Muscle Radiation Attenuation (SMRA) (Concordance Correlation Coefficient (CCC) = 0.92), Visceral Adipose Tissue index (VATI) (CCC = 0.99) and Subcutaneous Adipose Tissue Index (SATI) (CCC = 0.99). In conclusion, this article showed an automated and accurate segmentation system to segment the cross-sectional muscle and adipose area L3 lumbar spine level on abdominal CT. Future perspectives will include fine-tuning the algorithm and minimizing the outliers.
ISSN:1424-8220
1424-8220
DOI:10.3390/s21062083