How experts' mental model affects 3D image segmentation

Abstract

3D image segmentation is a fundamental process in many scientific and medical applications. Automatic algorithms do exist, but there are many use cases where these algorithms fail. The gold standard is still manual segmentation or review. Unfortunately, existing 3D segmentation tools do not currently take into account human mental models, low-level perception actions, and higher-level cognitive tasks. Our goal is to improve the quality and efficiency of manual segmentation by analyzing the process in terms of human mental models and low-level perceptual tasks. Preliminary results from our in-depth field studies suggest that compared to novices, experts have a stronger mental model of the 3D structures they segment. To validate this assumption, we introduce a novel test instrument to explore experts' mental model in the context of 3D image segmentation. We use this test instrument to measure individual differences in various spatial segmentation and visualization tasks. The tasks involve identifying valid 2D contours, slicing planes and 3D shapes.