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Automated assessment of thigh composition using machine learning for Dixon magnetic resonance images

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Magnetic Resonance Materials in Physics, Biology and Medicine Aims and scope Submit manuscript

Abstract

Objectives

To develop and validate a machine learning based automated segmentation method that jointly analyzes the four contrasts provided by Dixon MRI technique for improved thigh composition segmentation accuracy.

Materials and methods

The automatic detection of body composition is formulized as a three-class classification issue. Each image voxel in the training dataset is assigned with a correct label. A voxel classifier is trained and subsequently used to predict unseen data. Morphological operations are finally applied to generate volumetric segmented images for different structures. We applied this algorithm on datasets of (1) four contrast images, (2) water and fat images, and (3) unsuppressed images acquired from 190 subjects.

Results

The proposed method using four contrasts achieved most accurate and robust segmentation compared to the use of combined fat and water images and the use of unsuppressed image, average Dice coefficients of 0.94 ± 0.03, 0.96 ± 0.03, 0.80 ± 0.03, and 0.97 ± 0.01 has been achieved to bone region, subcutaneous adipose tissue (SAT), inter-muscular adipose tissue (IMAT), and muscle respectively.

Conclusion

Our proposed method based on machine learning produces accurate tissue quantification and showed an effective use of large information provided by the four contrast images from Dixon MRI.

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Acknowledgments

This study was funded by Lee Foundation Grant 2013 (GERILABS—Longitudinal Assessment of Biomarkers for characterisation of early Sarcopenia and predicting frailty and functional decline in community-dwelling Asian older adults Study).

We would like to acknowledge Assistant Professor Poh Chueh Loo from Nanyang Technological University for his valuable advices in segmentation techniques. We would also like to extend our thanks to Mr. Samuel Neo, Medical Social Worker, Department of Continuing and Community Care, Tan Tock Seng Hospital, for his assistance in recruitment through the various Senior Activity Centres (SAC). We extend our appreciation to the following SACs (Wesley SAC, Care Corner SAC, Xin Yuan Community Service, Potong Pasir Wellness Centre, Tung Ling Community Services (Marine Parade and Bukit Timah), Viriya Community Services-My Centre@Moulmein, House of Joy) and the study participants who have graciously consented to participation in the study.

Author information

Authors and Affiliations

  1. Institute of Geriatrics and Active Ageing, Tan Tock Seng Hospital, Singapore, Singapore

    Yu Xin Yang, Mei Sian Chong, Laura Tay, Suzanne Yew & Audrey Yeo

  2. Department of Geriatric Medicine, Tan Tock Seng Hospital, Singapore, Singapore

    Mei Sian Chong & Laura Tay

  3. Department of Diagnostic Radiology, Tan Tock Seng Hospital, Singapore, Singapore

    Cher Heng Tan

Authors
  1. Yu Xin Yang
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  2. Mei Sian Chong
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  3. Laura Tay
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  4. Suzanne Yew
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Corresponding author

Correspondence to Yu Xin Yang.

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The authors declare that they have no conflict of interest.

Ethical standards

All procedures performed in studies involving human participants were in accordance with the ethical standards of the institutional and/or national research committee and with the 1964 Helsinki declaration and its later amendments or comparable ethical standards.

Informed consent

Informed consent was obtained from all individual participants included in the study.

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Yang, Y.X., Chong, M.S., Tay, L. et al. Automated assessment of thigh composition using machine learning for Dixon magnetic resonance images. Magn Reson Mater Phy 29, 723–731 (2016). https://doi.org/10.1007/s10334-016-0547-2

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  • DOI: https://doi.org/10.1007/s10334-016-0547-2

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