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

IF 3.3 Q2 ENGINEERING, BIOMEDICAL International Journal of Biomedical Imaging Pub Date : 2019-09-01 DOI:10.1155/2019/1464592
Mario Amrehn, S. Steidl, Reinier Kortekaas, Maddalena Strumia, M. Weingarten, M. Kowarschik, A. Maier
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引用次数: 6

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.
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交互式图像分割系统的半自动化可用性评估框架
对于复杂的分割任务,完全自动化系统的可实现精度是固有的有限的。具体来说,当需要对少量给定数据集进行精确分割时,半自动方法对用户有明显的好处。人机交互优化是交互式图像分割的重要组成部分。然而,介绍新型交互式分割系统(ISS)的出版物往往缺乏对HCI方面的客观比较。研究表明,即使整个交互式原型的底层分割算法相同,其用户体验也可能有很大差异。因此,用户更喜欢简单的界面以及相当大的自由度来控制分割的每个迭代步骤。本文在广泛的用户研究的基础上,提出了一种客观的ISS比较方法。总结性的定性内容分析是通过抽象参与者给出的视觉和言语反馈来进行的。用户通过系统可用性量表(SUS)和AttrakDiff-2问卷对分割系统进行直接评估。此外,还介绍了这些研究中关于可用性方面的发现的近似值,仅从系统可测量的用户在使用交互式分割原型期间的行为中进行。所有问卷结果的预测平均相对误差为8.9%,接近问卷结果本身的预期精度。这种自动化评估方案可以显著减少调查原型的用户界面(UI)特征和分割方法的每个变化所需的资源。
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来源期刊
CiteScore
12.00
自引率
0.00%
发文量
11
审稿时长
20 weeks
期刊介绍: The International Journal of Biomedical Imaging is managed by a board of editors comprising internationally renowned active researchers. The journal is freely accessible online and also offered for purchase in print format. It employs a web-based review system to ensure swift turnaround times while maintaining high standards. In addition to regular issues, special issues are organized by guest editors. The subject areas covered include (but are not limited to): Digital radiography and tomosynthesis X-ray computed tomography (CT) Magnetic resonance imaging (MRI) Single photon emission computed tomography (SPECT) Positron emission tomography (PET) Ultrasound imaging Diffuse optical tomography, coherence, fluorescence, bioluminescence tomography, impedance tomography Neutron imaging for biomedical applications Magnetic and optical spectroscopy, and optical biopsy Optical, electron, scanning tunneling/atomic force microscopy Small animal imaging Functional, cellular, and molecular imaging Imaging assays for screening and molecular analysis Microarray image analysis and bioinformatics Emerging biomedical imaging techniques Imaging modality fusion Biomedical imaging instrumentation Biomedical image processing, pattern recognition, and analysis Biomedical image visualization, compression, transmission, and storage Imaging and modeling related to systems biology and systems biomedicine Applied mathematics, applied physics, and chemistry related to biomedical imaging Grid-enabling technology for biomedical imaging and informatics
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