Automatic muscle and fat segmentation in 3D abdominal CT images for body composition assessment
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2020-08-28
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en
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Abstract
Body composition is an informative biomarker in
the treatment of cancer. In particular, low muscle mass has
been associated with higher chemotherapy toxicity, shorter
time to tumor progression, poorer surgical outcomes, impaired
functional status, and shorter survival. However,
because CT-based body composition assessment requires
outlining the different tissues in the image, which is timeconsuming,
its practical value is currently limited. To form
an estimate of body composition, different tissues are often
segmented manually in a single 2D slice from the abdomen.
For use in both routine care and in research studies,
automatic segmentation of the different tissue types in the
abdomen is desirable. This study focuses on the development
and testing of an automatic approach to segment muscle and
fat tissue in the entire abdomen. The four classes of interest
are skeletal muscle (SM), inter-muscular adipose tissue
(IMAT), visceral adipose tissue (VAT), and subcutaneous
adipose tissue (SAT). A deep neural network is trained on
two-dimensional CT slices at the level of the third lumbar
vertebra. Three experiments were carried out with the goal
of improving the network with information from other,
unannotated data sources. Active learning methods were
applied to sample additional data to annotate and include in
the training of the model. The proposed algorithm combines
two models to segment muscle and fat in the entire abdomen
and achieves state-of-the-art results. Dice scores of 0.91,
0.84, 0.97, and 0.97 were attained for SM, IMAT, VAT, and
SAT, respectively, averaged over five locations throughout
the abdomen.
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Faculteit der Sociale Wetenschappen