A review of the challenges in deep learning for skeletal and smooth muscle ultrasound images

Peter Ardhianto, Jen Yung Tsai, Chih Yang Lin, Ben Yi Liau, Yih Kuen Jan, Veit Babak Hamun Akbari, Chi Wen Lung

Research output: Contribution to journalReview articlepeer-review

Abstract

Deep learning has aided in the improvement of diagnosis identification, evaluation, and the interpretation of muscle ultrasound images, which may benefit clinical personnel. Muscle ultrasound images presents challenges such as low image quality due to noise, insufficient data, and different characteristics between skeletal and smooth muscles that can affect the effectiveness of deep learning results. From 2018 to 2020, deep learning has the improved solutions used to overcome these challenges; however, deep learning solutions for ultrasound images have not been compared to the conditions and strategies used to comprehend the current state of knowledge for handling skeletal and smooth muscle ultrasound images. This study aims to look at the challenges and trends of deep learning performance, especially in regard to overcoming muscle ultrasound image problems such as low image quality, muscle movement in skeletal muscles, and muscle thickness in smooth muscles. Skeletal muscle segmentation presents difficulties due to the regular movement of muscles and resulting noise, recording data through skipped connections, and modified layers required for upsampling. In skeletal muscle classification, the problems faced are area-specific, thus making a cropping strategy useful. Furthermore, there is no need to add additional layer modifications for smooth muscle segmentation as muscle thickness is the main problem in such cases.

Original languageEnglish (US)
Article number4021
JournalApplied Sciences (Switzerland)
Volume11
Issue number9
DOIs
StatePublished - May 2021

Keywords

  • Classification method
  • Muscle disease
  • Network architecture
  • Segmentation framework
  • Ultrasonography

ASJC Scopus subject areas

  • Materials Science(all)
  • Instrumentation
  • Engineering(all)
  • Process Chemistry and Technology
  • Computer Science Applications
  • Fluid Flow and Transfer Processes

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