Designing a vascular segmentation "lookalike" task for non-medical users.

For an overview of IO devices, possible interaction methods, and first experiences with them, see the section on IO devices.

Description of the task:

The task involves a vessel network (typically a vessel with at least one bifurcation). A particular path through the vessel structure is automatically chosen by the centerline algorithm. This usually determines the appropriate vessel centerline correctly, so no user intervention is required. Then, the diameter and shape of the vessel is automatically determined, but with possible errors. Then, the user has to check the vessel segmentation manually. If the vessel shape is incorrect, it has to be edited manually with help of the scan data.

The task involves:

We want to mimick the segmentation task as closely as possible. However, can a segmentation-like 3D interpretation task be done by novices, and, if so, is the experience of novices transferable to experience by experts? In particular, experts are used to working with slice data, which may be difficult to interpret by beginners but much easier with experience. So, novices may prefer a 3D view while experts will not.

This means we cannot use the relative performance of slice vs 3D of our users to predict the relative performance of medical experts. What we can test with novices is how different kinds of novel representation and navigation methods compare.

I propose a simple task that has some of the more interesting properties of the segmentation task. This is a maze interpretation task. At its basis is a traditional orthogonal rectangular maze (but in 3D, a cube maze). The idea is that interpreting a 3D maze is roughly similar to vessel interpretation, but a maze should be interpretable by any type of user. In particular, the maze highlights interpretation of the overall vascular structure and its relationship with individual bifurcations that need closer examination.

This type of maze has similarities to a vascular network, in that it is basically a tree. Complex vascular networks look a lot like these 3D mazes. For example:

The mazes I generate now look like this:
(click to enlarge)

Some example tasks:

The "exhaustive search" nature of the task may be expressed by enabling a "cursor" to slide along the path to be examined, for example using the cursor keys. I am not sure whether existing medical software uses this, or only enables a slice to be selected by clicking on a length projection (typically the stretched view) with the mouse.

Research questions that we can readily address:

Other research questions which we want to do with experts:

Considerations regarding input devices

With the basic maze task that I have now, where the user interprets the model and has to select certain junctions, I have the following two main interactions: It does not seem necessary to support model translation in addition to rotation, since the model is small and does not require extensive navigation.

It seems best to do the selection wth the mouse. A 3D input device does not seem attractive. The literature suggests that a 2D position device is best. See also "selection using a one-eyed cursor in a fish tank VR environment". For the combination with rotation I currently have the following set of "most promising options":

Here are some devices I decided to exclude from the list: