Dataset Information

Fully Automated and Standardized Segmentation of Adipose Tissue Compartments via Deep Learning in 3D Whole-Body MRI of Epidemiologic Cohort Studies.

ABSTRACT:

Purpose

To enable fast and reliable assessment of subcutaneous and visceral adipose tissue compartments derived from whole-body MRI.

Materials and methods

Quantification and localization of different adipose tissue compartments derived from whole-body MR images is of high interest in research concerning metabolic conditions. For correct identification and phenotyping of individuals at increased risk for metabolic diseases, a reliable automated segmentation of adipose tissue into subcutaneous and visceral adipose tissue is required. In this work, a three-dimensional (3D) densely connected convolutional neural network (DCNet) is proposed to provide robust and objective segmentation. In this retrospective study, 1000 cases (average age, 66 years ± 13 [standard deviation]; 523 women) from the Tuebingen Family Study database and the German Center for Diabetes research database and 300 cases (average age, 53 years ± 11; 152 women) from the German National Cohort (NAKO) database were collected for model training, validation, and testing, with transfer learning between the cohorts. These datasets included variable imaging sequences, imaging contrasts, receiver coil arrangements, scanners, and imaging field strengths. The proposed DCNet was compared to a similar 3D U-Net segmentation in terms of sensitivity, specificity, precision, accuracy, and Dice overlap.

Results

Fast (range, 5-7 seconds) and reliable adipose tissue segmentation can be performed with high Dice overlap (0.94), sensitivity (96.6%), specificity (95.1%), precision (92.1%), and accuracy (98.4%) from 3D whole-body MRI datasets (field of view coverage, 450 × 450 × 2000 mm). Segmentation masks and adipose tissue profiles are automatically reported back to the referring physician.

Conclusion

Automated adipose tissue segmentation is feasible in 3D whole-body MRI datasets and is generalizable to different epidemiologic cohort studies with the proposed DCNet.Supplemental material is available for this article.© RSNA, 2020.

SUBMITTER: Kustner T

PROVIDER: S-EPMC8082356 | biostudies-literature |

REPOSITORIES: biostudies-literature

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