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

TL;DR

This paper proposes a semi-automated, objective evaluation framework for interactive image segmentation systems, combining user studies, feedback analysis, and measurable user actions to assess usability and compare different prototypes efficiently.

Contribution

It introduces an automated evaluation method for ISS that reduces resource use and provides objective comparisons based on user feedback and measurable actions.

Findings

User preferences favor simple interfaces and control over segmentation steps.

The automated evaluation predicts questionnaire results with 8.9% error.

The framework enables efficient comparison of different ISS prototypes.

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.