Abstract
Quantification and distribution of the thigh adipose tissues in CT images have clinical implication in prognostic chronic disease including type 2 diabetes and osteoarthritis. Although there are studies in the literature addressing the quantification of thigh tissues, only a handful of them aims to segment and quantify thigh adipose tissues in CT images automatically. In this study, we propose an automated technique for the segmentation and quantification of muscle, inter- and intra-muscular adipose tissue and subcutaneous adipose tissue in thigh CT images. Our technique combines morphological operations, thresholding, a Gaussian mixture model and the use of an accumulator matrix to map the number of adipose tissue pixels about muscle pixels and thus, to allow an automatic differentiation between SAT and Inter-MAT. Our method was quantitatively assessed using 144 thigh images extracted from 72 leg (left and right) CT scans. All images were manually segmented and the tissues quantified by a specialist with the help of a computer software and used for further comparative analysis. Our technique obtained precision of 0.998 and 0.982, respectively, for the fascia and thigh regions with corresponding recall values of 0.978 and 0.975. Also, the Dice similarity coefficient for both areas was close to 0.98.
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Notes
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Fascia is a sheet of connective tissue beneath the skin that attaches, stabilizes, encloses, and separates muscles and other internal organs.
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Acknowledgments
The authors would like to thank the São Paulo Research Foundation (FAPESP) (grant number 2016/15661-0) and the Coordination for the Improvement of Higher Education Personnel (CAPES) for the finantial support of this research.
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de Carvalho Felinto, J. et al. (2018). Automatic Segmentation and Quantification of Thigh Tissues in CT Images. In: Gervasi, O., et al. Computational Science and Its Applications – ICCSA 2018. ICCSA 2018. Lecture Notes in Computer Science(), vol 10960. Springer, Cham. https://doi.org/10.1007/978-3-319-95162-1_18
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