Abstract
Lower limb muscles provide various significant physiological functions, which are the key muscles to support daily activities. Segmentation of these muscles offers quantitative analysis for several clinical applications (e.g., understanding of musculoskeletal diseases). In recent studies on muscle tissues, water-fat quantitative magnetic resonance imaging (MRI) is frequently used as the main image modality due to its capability in distinguishing muscle and fat from their surrounding tissues. However, manual muscle segmentation in MRI is time-consuming and often requires professional knowledge. In this paper, an ensemble framework of combining two patch-based binary-class deep convolutional neural networks with the same architecture is proposed to achieve thigh and calf muscle segmentation from whole-body water-fat (mDIXON) MRIs. We compared our model to a state-of-the-art multi-class 3D U-Net model using a 5-fold cross-validation. A Dice coefficient of 0.9042 was achieved by our method, which was significantly more accurate than the multi-class 3D U-Net method. Additionally, the model deployment costs approximately 13.8 s per case.
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Acknowledgement
This research was funded by the NIHR Nottingham Biomedical Research Centre and carried out at/supported by the NIHR Nottingham Clinical Research Facilities. The views expressed are those of the author(s) and not necessarily those of the NHS, the NIHR or the Department of Health and Social Care. Funding was also provided by the Centre for musculoskeletal Ageing Research, University of Nottingham to support the PhD of Rosemary Nicholas.
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Gong, Z., Nicholas, R., Francis, S.T., Chen, X. (2022). Thigh and Calf Muscles Segmentation Using Ensemble of Patch-Based Deep Convolutional Neural Network on Whole-Body Water-Fat MRI. In: Yang, G., Aviles-Rivero, A., Roberts, M., Schönlieb, CB. (eds) Medical Image Understanding and Analysis. MIUA 2022. Lecture Notes in Computer Science, vol 13413. Springer, Cham. https://doi.org/10.1007/978-3-031-12053-4_20
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