Uncertainty Visualization

[mahsageshvadi]

All predictions inherently have some level of uncertainty, but often this uncertainty is disregarded as it doesn't significantly affect most practical applications. However, in certain critical areas like medical imaging, such as tumor resection surgery, where precision is important, considering uncertainty becomes crucial.

Our project focuses on addressing the phenomenon known as brain shift through advanced visualization techniques. During tumor resection surgery, the shape of the brain changes due to various factors. Consequently, relying on MRI images for navigation becomes unfeasible. To address this, we use registration techniques to predict an updated MRI view of the brain. However, these predictions come with inherent uncertainty, which must be accounted for. Now, the challenge is to visualize uncertainty.

We've developed a tool for visualizing uncertainty in 3D Slicer specifically designed for tumor resection surgery. Surgeons can explore various features to find the one that suits their needs best. Additionally, it's designed to work with all types of medical images.

How do surgeons trust our visualization?

We need to evaluate different visualizations to encourage surgeons to use them. We cannot assess them in a real setting because we cannot risk patients' quality and quantity of life. So, we developed a game within our tool where users can engage in a simulation task: carving out the tumor from a shifted medical image and earning points. They can compare different visualization techniques and determine which one is most helpful for them.

Link to our tool's code:
https://github.com/mahsageshvadi/UncertaintyVisualization

Screen shot of our evaluation game