Research to understand how we Infer 2D-cross sections from 3D shapes published in Human Factors journal

Understanding 2D cross-sections of 3D structures is important in medical imaging, biology, geology, art, architecture, physics and engineering. If you’ve ever had an MRI, your radiologist understood 3D spatial information from the 2D image slices that make up that medical imaging. 

Identifying and orienting the correct 2D cross-section of a 3D object is a complex set of spatial/visualization skills. In our collaboration with Dr. Anahita Sanadaji (Ohio University), Dr. Cindy Grimm (Oregon State University), and Dr. Christopher Sanchez (Oregon State University) we explore the potential for developing this expertise. Our work creates a computer-based training tool for inferring 2D cross-sections of complex #3D structures and evaluates it’s performance on a range of tasks. We show that it is possible to design and develop a task-based training tool in an interactive 3D user-interface for training the spatial/visualization skills to infer 2D cross sections from 3D shapes, we conduct a user study to validate the design and its effectiveness in training this skill, and we identify the specific skills that are enhanced after using our interactive training tool.

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


Anahita Sanandaji , Ohio University, Athens, USA

Cindy Grimm, Oregon State University, Corvallis, USA

Ruth West, University of North Texas, Denton, USA

Christopher A. Sanchez , Oregon State University, Corvallis, USA

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.

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