Correction of motion artifacts in ophthalmologic imaging procedures

Context

The project will examine the compensation of movement artifacts in ophthalmologic imaging procedures for further diagnostics as well as the measurement of eye movements for the early detection of retinal diseases. Even during fixation, the human eye is subject to continuous eye movements, which are crucial for the perception and interpretation of visual information. However, these fixation eye movements impair ophthalmologic imaging and cause movement artifacts in the images that affect the accuracy and reliability of further analysis or diagnostic procedures. For this reason, new methods are developed to extract eye movements from artifacts and compensate them. This makes it possible to analyze the effects of retinal diseases on the control of eye movements and can contribute to the development of early detection methods.

Tasks

The following challenges await you within the project:

• You will design and train intelligent algorithms for the correction of motion artifacts in OCT data.
• You will research methods for measuring the underlying motion of such artifacts.
• You will develop a data management system and create ground truth data for machine learning.

Requirements

• You are motivated, self-initiated and work conscientiously
• You have experience with Python and machine learning
• Your studies focus on information technology, signal processing or computer vision