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Title: The implementation of an AR (augmented reality) approach to support mammographic interpretation training - an initial feasibility study
Authors: Tang, Qiang
Chen, Yan
Gale, Alastair G.
Keywords: AR
Augmented Reality
Mammographic interpretation
Training
Issue Date: 2017
Publisher: © SPIE
Citation: TANG, Q., CHEN, Y. and GALE, A.G., 2017. The implementation of an AR (augmented reality) approach to support mammographic interpretation training - an initial feasibility study. IN: Kupinski, M.A. and Nishikawa, R.M. (eds). Medical Imaging 2017: Image Perception, Observer Performance, and Technology Assessment, Proceedings of SPIE, Vol. 10136, 1013604, Orlando, Florida, United States, 10th March 2017, doi:10.1117/12.2255833.
Abstract: Appropriate feedback plays an important role in optimising mammographic interpretation training whilst also ensuring good interpretation performance. The traditional keyboard, mouse and workstation technical approach has a critical limitation in providing supplementary image-related information and providing complex feedback in real time. Augmented Reality (AR) provides a possible superior approach in this situation, as feedback can be provided directly overlaying the displayed mammographic images so making a generic approach which can also be vendor neutral. In this study, radiological feedback was dynamically remapped virtually into the real world, using perspective transformation, in order to provide a richer user experience in mammographic interpretation training. This is an initial attempt of an AR approach to dynamically superimpose pre-defined feedback information of a DICOM image on top of a radiologist’s view, whilst the radiologist is examining images on a clinical workstation. The study demonstrates the feasibility of the approach, although there are limitations on interactive operations which are due to the hardware used. The results of this fully functional approach provide appropriate feedback/image correspondence in a simulated mammographic interpretation environment. Thus, it is argued that employing AR is a feasible way to provide rich feedback in the delivery of mammographic interpretation training.
Description: Copyright © 2017 SPIE. Society of Photo Optical Instrumentation Engineers. One print or electronic copy may be made for personal use only. Systematic reproduction and distribution, duplication of any material in this paper for a fee or for commercial purposes, or modification of the content of the paper are prohibited.
Version: Published
DOI: 10.1117/12.2255833
URI: https://dspace.lboro.ac.uk/2134/24636
Publisher Link: http://dx.doi.org/10.1117/12.2255833
ISSN: 0277-786X
Appears in Collections:Conference Papers and Presentations (Computer Science)

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