Register now After registration you will be able to apply for this opportunity online.
This opportunity is not published. No applications will be accepted.
Development of an automated tool for optic nerve sheath analysis
Intracranial hypertension remains a life-threatening neurological condition even today. This project aims to develop an automated tool to analyse optic nerve sheath ultrasound images for use as a diagnostic tool to support clinicians in the treatment of this hypertension.
Keywords: Computational Tools
Python
Image Analysis
Product Development
ETH Zurich
Biomedical Engineering
Software
Mechanical Engineering
Computer Science
Signal Processing
Intracranial hypertension remains a life-threatening neurological condition even today. Optic nerve sheath diameter (ONSD) measurements via ultrasound (US) are an important diagnostic tool to predict the level of intracranial pressure (ICP) and the degree of neural damage. Although ultrasonic imaging is a user-friendly diagnostic tool, standardization and replicability of images remain poor. While there have been approaches made to create an automatically computed analysis system, a smart and comprehensive tool for physicians and researchers is still missing. This project aims to create an automated method for fast and reliable ONSD analysis.
Working with engineers at ETH, surgeons at USZ, and researchers at UZH, you will get the chance to see firsthand how technology will continue to shape medicine for a modern world. We are a motivated team, eager to make an impact on medicine. This project has the possibility to be taken further, but the groundwork must first be lain.
Intracranial hypertension remains a life-threatening neurological condition even today. Optic nerve sheath diameter (ONSD) measurements via ultrasound (US) are an important diagnostic tool to predict the level of intracranial pressure (ICP) and the degree of neural damage. Although ultrasonic imaging is a user-friendly diagnostic tool, standardization and replicability of images remain poor. While there have been approaches made to create an automatically computed analysis system, a smart and comprehensive tool for physicians and researchers is still missing. This project aims to create an automated method for fast and reliable ONSD analysis.
Working with engineers at ETH, surgeons at USZ, and researchers at UZH, you will get the chance to see firsthand how technology will continue to shape medicine for a modern world. We are a motivated team, eager to make an impact on medicine. This project has the possibility to be taken further, but the groundwork must first be lain.
We are aiming to create an automated method for fast and reliable optic nerve sheath diameter analysis. This tool should be able to calculate characteristics of the optic nerve sheath (diameter, area, skewness, etc) and compare it to the intracranial pressure acquired at the same timestamp and evaluate whether or not any statistical correlation exists.
This task relies heavily on computational skills and therefore have a few as prerequisite (image analysis, filtering) but will be used in a real-world diagnostic scenario. You will be working directly with USZ researchers to identify the problems in which this tool should address.
Post thesis, we envision this tool to be used in actual preclinical trials that we are currently conducting at USZ! Then we will discuss if/how to take it further in the development process.
We are aiming to create an automated method for fast and reliable optic nerve sheath diameter analysis. This tool should be able to calculate characteristics of the optic nerve sheath (diameter, area, skewness, etc) and compare it to the intracranial pressure acquired at the same timestamp and evaluate whether or not any statistical correlation exists.
This task relies heavily on computational skills and therefore have a few as prerequisite (image analysis, filtering) but will be used in a real-world diagnostic scenario. You will be working directly with USZ researchers to identify the problems in which this tool should address.
Post thesis, we envision this tool to be used in actual preclinical trials that we are currently conducting at USZ! Then we will discuss if/how to take it further in the development process.
Prerequisites: 1. Experience and interest in computational methods specific to image analysis (python preferred) 2. Ability and interest to work in an interdisciplinary team of clinicians and engineers 3. Desire to have your product used in an actual preclinical setting! 4. Systematic and logical approach to problems, ability to integrate well into a team, curious mind.
What you'll learn: 1. How technology shapes modern medicine. We live in an age of customization - it is unfair to patients to receive anything less than customized care. 2. How image analysis can be used to improve patient care 3. What clinicians require in their diagnostic tools in modern times 4. How interdisciplinary teams work - true experience in combining those with differing backgrounds behind a common goal
The Biomedical Systems group at the pdz focused on biomedical problems that affect our world and how we can better address them using the state-of-the-art. From Hydrocephalus to Low Cost Ventilators, we aim to improve treatment options for those inflicted with problems yet to be solved. With strong collaborations in industry and academia, we are at the forefront of biomedical-related research across the world.
The Biomedical Systems group at the pdz focused on biomedical problems that affect our world and how we can better address them using the state-of-the-art. From Hydrocephalus to Low Cost Ventilators, we aim to improve treatment options for those inflicted with problems yet to be solved. With strong collaborations in industry and academia, we are at the forefront of biomedical-related research across the world.
Work on an interdisciplinary project in an interdisciplinary team using state-of-the-art methods to create a product with a real-life use case with active integration into our project team. This is a collaboration between USZ and PDZ.
Please send all queries to both:
Anthony Podgorsak
ETH Zürich
apodgorsak@ethz.ch
Nina Eva Trimmel
USZ
Nina.Trimmel@usz.ch