ARUS - Automatic Robotic Ultrasound Scanning for Muscle Segmentation and Recognition

MSc assignment


In the realm of medical imaging, the pursuit of non-invasive and efficient diagnostic tools has led to the integration of robotics with ultrasound technology. As the demand for accurate muscle segmentation and recognition in clinical settings continues to grow, there is a pressing need for automated systems that can enhance the precision and speed of ultrasound scanning. This study delves into the development of an Automatic Robotic Ultrasound Scanning (ARUS) system, aiming to revolutionize the field by combining the capabilities of robotics and ultrasound imaging. The convergence of these technologies holds the promise of not only expediting the scanning process but also improving the accuracy of muscle segmentation and recognition, thereby facilitating more effective clinical assessments.


This research is driven by the following objectives:

  • To design and implement an Automatic Robotic Ultrasound Scanning (ARUS) system that seamlessly integrates robotic control with ultrasound imaging for muscle examination.
  • To develop a robust muscle segmentation algorithm capable of accurately delineating muscle structures from ultrasound images acquired through the ARUS system.
  • To design and implement a muscle recognition algorithm that can identify specific muscle groups based on the segmented ultrasound images.

Expected Outcomes:

Upon successful completion of this study, we anticipate the following outcomes:

A Functional ARUS System: A fully operational automatic robotic ultrasound scanning system capable of performing efficient and accurate muscle examinations.

Advanced Muscle Segmentation Algorithm: A novel muscle segmentation algorithm exhibiting high accuracy and efficiency in delineating muscle structures from ultrasound images.

Effective Muscle Recognition Algorithm: An innovative muscle recognition algorithm demonstrating the ability to identify and classify specific muscle groups based on the segmented ultrasound images.

This study aims to not only present a technological innovation in robotics but also contribute valuable insights to the broader field of medical imaging, fostering advancements that can positively impact healthcare practices on new ventures.