A Semi-Automated Usability Evaluation Framework for Interactive Image Segmentation Systems

Abstract

For complex segmentation tasks, the achievable accuracy of fully automated systems is inherently limited. Specifically, when a precise segmentation result is desired for a small amount of given data sets, semi-automatic methods exhibit a clear benefit for the user. The optimization of human computer interaction (HCI) is an essential part of interactive image segmentation. Nevertheless, publications introducing novel interactive segmentation systems (ISS) often lack an objective comparison of HCI aspects. It is demonstrated that even when the underlying segmentation algorithm is the same throughout interactive prototypes, their user experience may vary substantially. As a result, users prefer simple interfaces as well as a considerable degree of freedom to control each iterative step of the segmentation. In this article, an objective method for the comparison of ISS is proposed, based on extensive user studies. A summative qualitative content analysis is conducted via abstraction of visual and verbal feedback given by the participants. A direct assessment of the segmentation system is executed by the users via the system usability scale (SUS) and AttrakDiff-2 questionnaires. Furthermore, an approximation of the findings regarding usability aspects in those studies is introduced, conducted solely from the system-measurable user actions during their usage of interactive segmentation prototypes. The prediction of all questionnaire results has an average relative error of 8.9%, which is close to the expected precision of the questionnaire results themselves. This automated evaluation scheme may significantly reduce the resources necessary to investigate each variation of a prototype’s user interface (UI) features and segmentation methodologies.

Exploring Generative Design for Assistive Devices

“…There is a need to identify methods that are able to quickly assess hundreds or thousands of design possibilities and rank them similarly to how they would be with a human data driven tool such as SUS. Some work in this direction has been done within very specific contexts, such as usability evaluation of new interfaces for image segmentation systems, such as those used to identify anomalies in medical scans (Amrehn, et. al.…”

Individual fibre inclination segmentation from X-ray computed tomography using principal component analysis

“…In a study by Amrehn et al, 23 it was concluded that interactive segmentation methods are beneficial for complex structures as opposed to fully automated segmentation methods that inherently lack domain knowledge.…”