Preprint Article Version 3 This version is not peer-reviewed

Estimating Full Regional Skeletal Muscle Fibre Orientation from B-Mode Ultrasound Images Using Convolutional, Residual, and Deconvolutional Neural Networks

Version 1 : Received: 8 November 2017 / Approved: 8 November 2017 / Online: 8 November 2017 (04:36:50 CET)
Version 2 : Received: 15 November 2017 / Approved: 16 November 2017 / Online: 16 November 2017 (04:34:20 CET)
Version 3 : Received: 19 January 2018 / Approved: 19 January 2018 / Online: 19 January 2018 (14:05:16 CET)

How to cite: Cunnigham, R.; Sánchez, M.B.; May, G.; Loram, I. Estimating Full Regional Skeletal Muscle Fibre Orientation from B-Mode Ultrasound Images Using Convolutional, Residual, and Deconvolutional Neural Networks. Preprints 2017, 2017110053 (doi: 10.20944/preprints201711.0053.v3). Cunnigham, R.; Sánchez, M.B.; May, G.; Loram, I. Estimating Full Regional Skeletal Muscle Fibre Orientation from B-Mode Ultrasound Images Using Convolutional, Residual, and Deconvolutional Neural Networks. Preprints 2017, 2017110053 (doi: 10.20944/preprints201711.0053.v3).

Abstract

Direct measurement of strain within muscle is important for understanding muscle function in health and disease. Current technology (kinematics, dynamometry, electromyography) provides limited ability to measure strain within muscle. Regional fiber orientation and length are related with active/passive strain within muscle. Currently, ultrasound imaging provides the only non-invasive means of observing regional fiber orientation within muscle during dynamic tasks. Previous attempts to automatically estimate fiber orientation from ultrasound are not adequate, often requiring manual region selection, feature engineering, providing low-resolution estimations (one angle per muscle), and deep muscles are often not attempted. Here, we propose deconvolutional neural networks (DCNN) for estimating fiber orientation at the pixel-level. Dynamic ultrasound images sequences of the calf muscles were acquired (25 Hz) from 8 healthy volunteers (4 male, ages: 25–36, median 30). A combination of expert annotation and interpolation/extrapolation provided labels of regional fiber orientation for each image. We then trained DCNNs both with and without dropout using leave one out cross-validation. Our results demonstrated robust estimation of regional fiber orientation with approximately 3° error, which was an improvement on previous methods. The methods presented here provide new potential to study muscle in disease and health.

Subject Areas

ultrasound; b-mode; skeletal muscle; fascicle orientation; pennation angle; fiber orientation; fiber tract; fascicle tract; convolutional neural network; deconvolutional neural network

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