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Machine Learning on Ultrasound Images

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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 Ultrasound Images acquired with a high-end Ultrasound research system, to build ML algorithms for the extraction of such physiological features.

Goal & Tasks

The goal of this project is to enable the automatic extraction of relevant physiological information (the length of muscle fascicles) directly from Ultrasound images. The main tasks of this project are:

  • Literature study of ultrasound imaging principles
  • Literature study of image analyses algorithm
  • Identification and implementation of alternative labeling algorithms


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

Status: Available

Looking for Interested Students
Supervision: Sergei Vostrikov, Andrea Cossettini


25% Literature Study
30% Image acquisition and processing
45% Training and testing of networks


Luca Benini

Practical Details

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