As medical imaging expands from 2D to 3D scanning technologies, it is important to understand how diagnostic information is represented and accessed – both perceptually and algorithmically – in images of different dimension. The historical emphasis on 2D images provides a foundation of evaluation methodologies and experimental findings that can be used as a reference for comparison with newer 3D approaches. The purpose of this special series is to explore this interface.

Anatomical knowledge and specialized visual skills, such as pattern recognition and efficient visual search strategies, are fundamental for the effective interpretation of medical images. However, it is not clear that the skills and techniques that lead to successful decision making in 2D imaging are the same as those that will lead to success in the reading of 3D images. This is true not only for human readers, but for computer-based “readers” as well. Algorithms developed for 2D images can fail when applied to 3D datasets. A limited set of studies to date have established that diagnostic features, human search patterns, and effects of reader expertise can change substantially between 2D and 3D images. Additionally, 3D volumetric images require a more complex display procedure to render 3D spatial relationships on a 2D monitor in a way that is most effective for the reader. Open questions in these areas and related topics have motivated us to focus new work in this area into a readily accessible series as a resource for building this field.

The objectives of the special series are to characterize the differences between the use of 2D and 3D imaging as it relates to understanding of human and computer decision-making processes. The relevant topics include – but are not limited to – visual search, image perception, observer performance, model observers, computer-aided detection, cognitive processes, deep and machine learning and image understanding.

The special series begins in this issue of the journal (see the articles “ 2-D CNN vs 3-D CNN for false-positive reduction in lung cancer screening” by Yu et al. and “ Eye tracking reveals expertise-related differences in the time-course of medical image inspection and diagnosis” by Brunyé et al.). We welcome submissions across the spectrum of disease processes and imaging modalities focused on understanding theoretical and practical impacts of image dimension. To this end, we hope to stimulate research into the role of image dimension on perceptual and cognitive processes of decision makers in different areas of medical image interpretation.