Towards the Semantic Enrichment of Free-Text Annotation of Image Quality Assessment for UK Biobank Cardiac Cine MRI Scans

• Published in: Deep Learning and Data Labeling for Medical Applications Vol. 10008; pp. 238 - 248
• Main Authors: Carapella, Valentina, Jiménez-Ruiz, Ernesto, Lukaschuk, Elena, Aung, Nay, Fung, Kenneth, Paiva, Jose, Sanghvi, Mihir, Neubauer, Stefan, Petersen, Steffen, Horrocks, Ian, Piechnik, Stefan
• Format: Book Chapter
• Language: English
• Published: Switzerland Springer International Publishing AG 2016; Springer International Publishing
• Series: Lecture Notes in Computer Science
• Subjects: Artificial intelligence; Automatic Image Quality Assessment; Biobank; Free Text Annotations; Image processing; Ontology Rules; Pattern recognition; Semantic Layer
• ISBN: 9783319469751; 3319469754
• ISSN: 0302-9743; 1611-3349
• DOI: 10.1007/978-3-319-46976-8_25

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.