Developing and Validating a Computer-Based Training Tool for Inferring 2D Cross-Sections of Complex 3D Structures

Objective: Developing and validating a novel domain-agnostic, computer-based training tool for enhancing 2D cross-section understanding of complex 3D structures.

Background: Understanding 2D cross-sections of 3D structures is a crucial skill in many disciplines, from geology to medical imaging . It requires a complex set of spatial/visualization skills including mental rotation, spatial structure understanding, and viewpoint projection. Prior studies show that experts differ from novices in these skills.

Method: We have developed a novel training tool for inferring 2D cross-sections of 3D structures using a participatory design methodology. We used a between-subject study design, with 60 participants, to evaluate the training tool. Our primary effectiveness evaluation was based on pre- and postspatial tests that measured both cross-section abilities and specific spatial skills: viewpoint, mental rotation, and card rotation.

Results: Results showed significant performance gains on inferring 2D cross-sections for participants of the training group. Our tool improves two other spatial skills as well: mental rotation and viewpoint visualization.

Conclusion: Our training tool was effective not only in enhancing 2D cross-section understanding of complex 3D structures, but also in improving mental rotation and viewpoint visualization skills.

Application: Our tool can be beneficial in different fields such as medical imaging, biology, geology, and engineering. For example, an application of our tool is in medical/research labs to train novice segmenters in ongoing manual 3D segmentation tasks. It can also be adapted in other contexts, such as training children, older adults, and individuals with very low spatial skills.