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Machine Learning for extracting Muscle features using Ultrasound

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Short Description

Ultrasound imaging is a non-invasive imaging technique that provides visible information on the structure of musculoskeletal tissues and organs. The development of wearable ultrasound probes would enable real-time non-invasive continuous monitoring of physiological parameters during the day, which is of particular interest for medical therapies and sport science. However, efficient machine learning (ML) algorithms are required in order to automatically extract the physiological parameters of interest (i.e., the length of muscle fascicles). In the context of this thesis, you will work with the raw data acquired by a fully-digital ultrasound probe, to build ML algorithm for the extraction of such physiological features.

Goal & Tasks

The goal of this project is to investigate the possibility of extracting relevant physiological information (the length of muscle fascicles) directly from raw Ultrasound data. The main tasks of this project are:

  • Literature study of ultrasound imaging principles
  • Neural network training and testing, based on available datasets of labeled images
  • Possible optimization and deployment of the network on an embedded platform


  • Basics of Machine Learning (required) and Deep Learning (desirable)
  • Python (sklearn, tensorflow)
  • C

Status: Completed

Soley Hafthorsdottir

Supervision: Sergei Vostrikov, Andrea Cossettini, Michael Rieder


15% Literature Study
10% Dataset processing
35% Training and testing of networks
40% Deployment on FPGA or microcontroller


Luca Benini

Practical Details

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