Download PDF
Methods Inf Med 2012; 51(06): 539-548
DOI: 10.3414/ME11-02-0025
Focus Theme – Original Articles
Schattauer GmbH

A Survey on Visual Information Search Behavior and Requirements of Radiologists

D. Markonis
1   University of Applied Sciences Western Switzerland (HES-SO), Switzerland
,
M. Holzer
2   Medical University of Vienna, Vienna, Austria
,
S. Dungs
3   University Duisburg-Essen, Germany
,
A. Vargas
4   Health On the Net Foundation (HON), Switzerland
,
G. Langs
2   Medical University of Vienna, Vienna, Austria
,
S. Kriewel
3   University Duisburg-Essen, Germany
,
H. Müller
1   University of Applied Sciences Western Switzerland (HES-SO), Switzerland
› Author Affiliations
Further Information

Publication History

received:31 August 2011

accepted:06 March 2012

Publication Date:
20 January 2018 (online)

    Permissions and Reprints

Summary

Objectives: The main objective of this study is to learn more on the image use and search requirements of radiologists. These requirements will then be taken into account to develop a new search system for images and associated meta data search in the Khresmoi project.

Methods: Observations of the radiology workflow, case discussions and a literature review were performed to construct a survey form that was given online and in paper form to radiologists. Eye tracking was performed on a radiology viewing station to analyze typical tasks and to complement the survey.

Results: In total 34 radiologists answered the survey online or on paper. Image search was mentioned as a frequent and common task, particularly for finding cases of interest for differential diagnosis. Sources of information besides the Internet are books and discussions with colleagues. Search for images is unsuccessful in around 25% of the cases, stopping the search after around 10 minutes. The most common reason for failure is that target images are considered rare. Important additions for search requested in the survey are filtering by pathology and modality, as well as search for visually similar images and cases. Few radiologists are familiar with visual retrieval but they desire the option to upload images for searching similar ones.

Conclusions: Image search is common in radiology but few radiologists are fully aware of visual information retrieval. Taking into account the many unsuccessful searches and time spent for this, a good image search could improve the situation and help in clinical practice.

Keywords

User behavior - survey - image retrieval - radiology
 

  • References

  • 1 Aisen AM, Broderick LS, Winer-Muram H, Brodley CE, Kak AC, Pavlopoulou C, Dy J, Shyu C-R, Marchiori A. Automated storage and retrieval of thin-section CT images to assist diagnosis: System description and preliminary assessment. Radiology 2003; 228 (01) 265-270.
    CrossrefPubMedGoogle Scholar
  • 2 Atkins MS, Moise A, Rohling R. An application of eye gaze tracking for designing radiologists’ workstations: Insights for comparative visual search tasks. ACM Transactions on Applied Perception (TAP) 2006; 3: 2
    PubMedGoogle Scholar
  • 3 Beard DV, Johnston RE, Toki O. A study of radiologists viewing multiple computed tomography examinations using an eyetracking device. Journal of Digital Imaging 1990; 3 (04) 230-237.
    CrossrefPubMedGoogle Scholar
  • 4 Beckers T, Fuhr N. User-oriented and eye-trackingbased evaluation of an interactive search system. In:. Workshop on Human-Computer Interaction and Information Retrieval. 2010
    Google Scholar
  • 5 Chbeir R, Amghar Y, Flory A. A four-dimensional approach to medical image retrieval. Methods Inf Med 2001; 40 (01) 178-183.
    Article in Thieme ConnectPubMedGoogle Scholar
  • 6 Choi Y, Rasmoussen EM. Searching for images: The analysis of users’ queries for image retrieval in American history. Journal of the American Society for Information Science and Technology, 2003; 54 (06) 498-511.
    CrossrefPubMedGoogle Scholar
  • 7 Cogdill KW, Friedman CP, Jenkins CG. Information needs and information seeking in community medical education. Academic Medicine, 2000; 75 (05) 484-486.
    CrossrefPubMedGoogle Scholar
  • 8 Cutrell E, Guan Z. What are you looking for?: an eye–tracking study of information usage in web search. In: Proceedings of the SIGCHI conference on Human factors in computing systems. 2007
    Google Scholar
  • 9 Datta R, Joshi D, Li J, Wang JZ. Image retrieval: Ideas, influences, and trends of the new age. ACM Computing Surveys 2008; 40 (02) 1-60.
    PubMedGoogle Scholar
  • 10 Detmer WM, Barnett OG, Hersh W. Medweaver: integrating decision support, literature searching, and web exploration using the UMLS metathesaurus. In: Proceedings of the AMIA Annual Fall Symposium. 1997: 490-494.
    Google Scholar
  • 11 Goodrun A, Spink A. Image searching on the excite web search engine. Information Processing & Management 2001; 37 (02) 295-311.
    CrossrefPubMedGoogle Scholar
  • 12 Gorman P. Information needs of physicians. Journal of the American Society for Information Science 1995; 46: 10
    PubMedGoogle Scholar
  • 13 Heathfield HA, Wyatt I. Philosophies for the design and development of clinical decision support systems. Methods Inf Med 1993; 32: 01
    Article in Thieme ConnectPubMedGoogle Scholar
  • 14 Hersh W, Müller H, Gorman P, Jensen J. Task analysis for evaluating image retrieval systems in the ImageCLEF biomedical image retrieval task. In: Slice of Life conference on Multimedia in Medical Education (SOL 2005). Portland, OR, USA: June 2005
    Google Scholar
  • 15 Hersh WR, Hickam DH. How well do physicians use electronic information retrieval systems?. Journal of the American Medical Association 1998; 280 (015) 1347-1352.
    CrossrefPubMedGoogle Scholar
  • 16 Haux R, Knaup P, Bauer AW, Herzog W, Reinhardt E, Uberla K, van Elmenen W, Wahlster W. Information processing in healthcare at the start of the third millennium: Potential and limitations. Methods Inf Med 2001; 40 (02) 156-162.
    Article in Thieme ConnectPubMedGoogle Scholar
  • 17 Kules B, Capra R, Banta M, Sierra T. What do exploratory searchers look at in a faceted search interface? In: Proceedings of the 9th ACM/IEEE-CS joint conference on Digital libraries. 2009
    Google Scholar
  • 18 Kundel HL, Nodine CF, Conant EF, aWeinstein SP. Holistic component of image perception in mammogram interpretation: Gaze-tracking study. Radiology 2007; 242: 396-402.
    CrossrefPubMedGoogle Scholar
  • 19 Kundel HL, Nodine CF, Krupinski EA. Using gaze-tracking data and mixture distribution analysis to support a holistic model for the detection of cancers on mammograms. Academic Radiology, 2008; 15 (07) 881-886.
    CrossrefPubMedGoogle Scholar
  • 20 Liang Chen H. An analysis of image queries in the field of art history. Journal of the American Society for Information Science and Technology 2000; 52 (03) 260-273.
    PubMedGoogle Scholar
  • 21 Markkula M, Sormunen E. Searching for photos - journalists’ practices in pictorial IR. In. Eakins JP, Harper DJ, Jose J. Editors The Challenge of Image Retrieval. A Workshop and Symposium on Image Retrieval. Electronic Workshops in Computing, Newcastle upon Tyne. The British Computer Society: 1998: 5
    Google Scholar
  • 22 Moise A, Atkins MS. Design requirements for radiology workstations. Journal of Digital Imaging 2004; 17 (02) 92-99.
    CrossrefPubMedGoogle Scholar
  • 23 Müller H, Boyer C, Gaudinat A, Hersh W. Geissbuhler A. Analyzing web log files of the Health On the Net HONmedia search engine to define typical image search tasks for image retrieval evaluation. In: MedInfo2007, volume 12 of IOS press, Studies in Health Technology and Informatics. Brisbane, Australia: 2007: 1319-1323.
    Google Scholar
  • 24 Mül ler H, Despont-Gros C, Hersh W, Jensen J, Lovis C, Geissbuhler A. Health care professionals’ image use and search behaviour. In: Proceedings of the Medical Informatics Europe Conference (MIE 2006), IOS Press, Studies in Health Technology and Informatics. Maastricht, The Netherlands: 2006. August 24-32.
    Google Scholar
  • 25 Müller H, Kalpathy-Cramer J, Hersh W, Geissbuhler A. Using Medline queries to generate image retrieval tasks for benchmarking. In: Medical Informatics Europe (MIE2008). Gothenburg, Sweden: IOS press; 2008: 523-528.
    Google Scholar
  • 26 Müller H. Report on image use behaviour and requirements, Deliverable D9.1 of the Khresmoi project. 2011
    Google Scholar
  • 27 Redfern RO, Horii SC, Feingold E. Radiology workflow and patient volume: Effect of picture archiving and communication systems on technologists and radiologists. Journal of Digital Imaging 2000; 13 (02) 97-100.
    CrossrefPubMedGoogle Scholar
  • 28 Reiner BL, Siegel EL, Flagle C. Effect of filmless imaging on the utilization of radiologic services. Radiology 2000; 215: 163-167.
    CrossrefPubMedGoogle Scholar
  • 29 Revere D, Turner AM, Madhavan A. Understanding the information needs of public health practitioners: A literature review to inform design of an interactive digital knowledge management system. Journal of Biomedical Informatics 2007; 40 (04) 410-421.
    CrossrefPubMedGoogle Scholar
  • 30 Smeulders AWM, Worring M, Santini S, Gupta A, Jain R. Content-based image retrieval at the end of the early years. IEEE Transactions on Pattern Analysis and Machine Intelligence 2000; 22 (012) 1349-1380.
    CrossrefPubMedGoogle Scholar
  • 31 Tourassi GD, Mazurowski MA, Harrawood BP. Exploring the potential of context-sensitive cade in screening mammography. Medical Physics, 2010; 37 (11) 5728-5736.
    CrossrefPubMedGoogle Scholar
  • 32 van Deursen AJAM, van Dijk JAGM. Internet skills performance tests: Are people ready for ehealth?. Journal of Medical Internet Research 2011; 13: 02
    CrossrefPubMedGoogle Scholar
  • 33 Wilson TD. On user studies and information needs. Journal of Documentation 1981; 37 (01) 3-15.
    CrossrefPubMedGoogle Scholar
  • 34 Yee K-P, Swearingen K, Li K, Hearst M. Faceted metadata for image search and browsing. In: Pro-ceedings of the SIGCHI conference on Human factors in computing systems. 2003
    Google Scholar