Kumar Ashnil, Kim Jinman, Bi Lei, Fulham Michael, Feng Dagan
Biomedical and Multimedia Information Technology (BMIT) Research Group, School of Information Technologies, University of Sydney, Sydney, Australia,
Int J Comput Assist Radiol Surg. 2013 Nov;8(6):1003-14. doi: 10.1007/s11548-013-0896-5. Epub 2013 May 7.
Content-based image retrieval (CBIR) in medicine has been demonstrated to improve evidence-based diagnosis, education, and teaching. However, the low clinical adoption of CBIR is partially because the focus of most studies has been the development of feature extraction and similarity measurement algorithms with limited work on facilitating better understanding of the similarity between complex volumetric and multi-modality medical images. In this paper, we present a method for defining user interfaces (UIs) that enable effective human user interpretation of retrieved images.
We derived a set of visualisation and interaction requirements based on the characteristics of modern volumetric medical images. We implemented a UI that visualised multiple views of a single image, displayed abstractions of image data, and provided access to supplementary non-image data. We also defined interactions for refining the search and visually indicating the similarities between images. We applied the UI for the retrieval of multi-modality positron emission tomography and computed tomography (PET-CT) images. We conducted a user survey to evaluate the capabilities of our UI.
Our proposed method obtained a high rating ( ≥ 4 out of 5) in the majority of survey questions. In particular, the survey responses indicated the UI presented all the information necessary to understand the retrieved images, and did so in an intuitive manner.
Our proposed UI design improved the ability of users to interpret and understand the similarity between retrieved PET-CT images. The implementation of CBIR UIs designed to assist human interpretation could facilitate wider adoption of medical CBIR systems.
医学领域基于内容的图像检索（CBIR）已被证明可改善循证诊断、教育及教学。然而，CBIR在临床中的应用率较低，部分原因在于大多数研究的重点是特征提取和相似性测量算法的开发，而在促进更好地理解复杂的体积医学图像和多模态医学图像之间的相似性方面所做的工作有限。在本文中，我们提出了一种定义用户界面（UI）的方法，该方法能够让用户有效地解读检索到的图像。
我们基于现代体积医学图像的特征得出了一组可视化和交互需求。我们实现了一个UI，它可以可视化单个图像的多个视图，显示图像数据的抽象信息，并提供对补充非图像数据的访问。我们还定义了用于优化搜索和直观显示图像之间相似性的交互。我们将该UI应用于多模态正电子发射断层扫描和计算机断层扫描（PET-CT）图像的检索。我们进行了一项用户调查，以评估我们UI的功能。
在大多数调查问题中，我们提出的方法获得了较高的评分（≥4分（满分5分））。特别是，调查反馈表明该UI以直观的方式呈现了理解检索到的图像所需的所有信息。
我们提出的UI设计提高了用户解读和理解检索到的PET-CT图像之间相似性的能力。旨在辅助人工解读的CBIR UI的实现可能会促进医学CBIR系统的更广泛应用。
