ArticleInternational journal of computer assisted radiology and surgery2013
Designing user interfaces to enhance human interpretation of medical content-based image retrieval: application to PET-CT images.
Article in International journal of computer assisted radiology and surgery, 2013. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
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Who cites it
2 citing papers in PubMed.
- Qualitative studies: designing a multimodal medical visualization tool for helping patients interpret 3D medical images.Frontiers in physiology · 2025Article
- Testing of the assisting software for radiologists analysing head CT images: lessons learned.BMC medical imaging · 2017Article
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Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
purposeContent-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.
methodsWe 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.
resultsOur 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.
conclusionOur 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.
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