Abstract
Image quality assessment is fundamental as it affects the level of confidence in any output obtained from image analysis. Clinical research imaging scans do not often come with an explicit evaluation of their quality, however reports are written associated to the patient/volunteer scans. This rich free-text documentation has the potential to provide automatic image quality assessment if efficiently processed and structured. This paper aims at showing how the use of Semantic Web technology for structuring free-text documentation can provide means for automatic image quality assessment. We aim to design and implement a semantic layer for a special dataset, the annotations made in the context of the UK Biobank Cardiac Cine MRI pilot study. This semantic layer will be a powerful tool to automatically infer or validate quality scores for clinical images and efficiently query image databases based on quality information extracted from the annotations. In this paper we motivate the need for this semantic layer, present an initial version of our ontology as well as preliminary results. The presented approach has the potential to be extended to broader projects and ultimately employed in the clinical setting.
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Notes
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CMR-QA ontology and related assets can be downloaded from https://github.com/ernestojimenezruiz/CMR-QA-Semantic-Layer.
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References
Petersen, S.E., et al.: Imaging in population science: cardiovascular magnetic resonance in 100,000 participants of UK Biobank - rationale, challenges and approaches. J. Cardiovasc. Magn. Reson. 15(1), 1–10 (2013). http://www.ukbiobank.ac.uk/
Petersen, S.E., et al.: UK biobank’s cardiovascular magnetic resonance protocol. J. Cardiovasc. Magn. Reson. 18(1), 8 (2016)
Schulz-Menger, J., et al.: Standardized image interpretation and post processing in cardiovascular magnetic resonance: society for cardiovascular magnetic resonance (SCMR) board of trustees task force on standardized post processing. J. Cardiovasc. Magn. Reson. 15(1), 1–19 (2013)
Gruber, T.R.: Toward principles for the design of ontologies used for knowledge sharing? Int. J. Hum. Comput. Stud. 43(5–6), 907–928 (1995)
Guarino, N., Oberle, D., Staab, S.: What is an ontology? In: Staab, S., Studer, R. (eds.) Handbook on Ontologies, pp. 1–17. Springer, Heidelberg (2009)
Noy, N.F., et al.: BioPortal: ontologies and integrated data resources at the click of a mouse. Nucleic Acids Res. 37(Web-Server-Issue), 170–173 (2009)
Giese, M., et al.: Optique: zooming in on big data. IEEE Comput. 48(3), 60–67 (2015)
Andrade, A.Q., Kreuzthaler, M., Hastings, J., Krestyaninova, M., Schulz, S.: Requirements for semantic biobanks. Stud. Health Technol. Inform. 180, 569–573 (2012)
Pathak, J., et al.: Applying semantic web technologies for phenome-wide scan using an electronic health record linked biobank. J. Biomed. Semant. 3, 10 (2012)
Brochhausen, M., et al.: Developing a semantically rich ontology for the biobank-administration domain. J. Biomed. Semant. 4, 23 (2013)
Müller, H., Reihs, R., Zatloukal, K., Jeanquartier, F., Merino-Martinez, R., van Enckevort, D., Swertz, M.A., Holzinger, A.: State-of-the-art and future challenges in the integration of biobank catalogues. In: Holzinger, A., Röcker, C., Ziefle, M. (eds.) Smart Health. LNCS, vol. 8700, pp. 261–273. Springer, Heidelberg (2015)
Spasic, I., Ananiadou, S., McNaught, J., Kumar, A.: Text mining and ontologies in biomedicine: making sense of raw text. Briefings Bioinf. 6(3), 239–251 (2005)
Bodenreider, O.: Lexical, terminological and ontological resources for biological text mining. In: Ananiadou, S., McNaught, J. (eds.) Text Mining for Biology and Biomedicine, pp. 43–66. Artech House, Boston (2006)
Nenov, Y., Piro, R., Motik, B., Horrocks, I., Wu, Z., Banerjee, J.: RDFox: a highly-scalable RDF store. In: Arenas, M., et al. (eds.) ISWC 2015. LNCS, vol. 9367, pp. 3–20. Springer, Heidelberg (2015). doi:10.1007/978-3-319-25010-6_1
Schulz, S., Cornet, R., Spackman, K.A.: Consolidating SNOMED CT’s ontological commitment. Appl. Ontology 6(1), 1–11 (2011)
Acknowledgements
SEP, SN and SP acknowledge the British Heart Foundation (BHF) for funding the manual analysis to create a cardiovascular magnetic resonance imaging reference standard for the UK Biobank imaging resource in 5,000 CMR scans (PG/14/89/31194, PI Petersen, 6/2015 to 5/2018). SKP, VC and SN were additionally funded by the National Institute for Health Research (NIHR) Oxford Biomedical Research Centre based at The Oxford University Hospitals Trust at the University of Oxford. EJR and IH were funded by the European Commission under FP7 Grant Agreement 318338, “Optique”, and the EPSRC projects Score!, ED3 and DBOnto.
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Carapella, V. et al. (2016). Towards the Semantic Enrichment of Free-Text Annotation of Image Quality Assessment for UK Biobank Cardiac Cine MRI Scans. In: Carneiro, G., et al. Deep Learning and Data Labeling for Medical Applications. DLMIA LABELS 2016 2016. Lecture Notes in Computer Science(), vol 10008. Springer, Cham. https://doi.org/10.1007/978-3-319-46976-8_25
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