A deep learning localization method for measuring abdominal muscle dimensions in ultrasound images

Saleh, Alzayat, Laradji, Issam H., Lammie, Corey, Vazquez, David, Flavell, Carol Ann, and Rahimi Azghadi, Mostafa (2021) A deep learning localization method for measuring abdominal muscle dimensions in ultrasound images. IEEE Journal of Biomedical and Health Informatics. (In Press)

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Abstract

Health professionals extensively use 2D US videos and images to visualize and measure internal organs for various purposes including evaluation of muscle architectural changes. US images can be used to measure abdominal muscles dimensions for the diagnosis and creation of customized treatment plans for patients with LBP, however, they are difficult to interpret. Due to high variability, skilled professionals with specialized training are required to take measurements to avoid low intra-observer reliability. This variability stems from the challenging nature of accurately finding the correct spatial location of measurement endpoints in abdominal US images. In this paper, we use a DL approach to automate the measurement of the abdominal muscle thickness in 2D US images. By treating the problem as a localization task, we develop a modified FCN architecture to generate blobs of coordinate locations of measurement endpoints, similar to what a human operator does. We demonstrate that using the TrA400 US image dataset, our network achieves a MAE of 0.3125 on the test set, which almost matches the performance of skilled ultrasound technicians. Our approach can facilitate next steps for automating the process of measurements in 2D US images, while reducing inter-observer as well as intra-observer variability for more effective clinical outcomes.

Item ID: 68402
Item Type: Article (Research - C1)
ISSN: 2168-2208
Keywords: Ultrasound, Transversus Abdominis, Musculoskeletal, Deep Learning, Convolutional Neural Network
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Copyright Information: (c) 2021 IEEE.
Funders: Australian Research Training Program (RTP) Scholarship
Date Deposited: 14 Jun 2021 00:11
FoR Codes: 40 ENGINEERING > 4099 Other engineering > 409902 Engineering instrumentation @ 75%
42 HEALTH SCIENCES > 4201 Allied health and rehabilitation science > 420106 Physiotherapy @ 25%
SEO Codes: 20 HEALTH > 2001 Clinical health > 200199 Clinical health not elsewhere classified @ 100%
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