Data preparation for artificial intelligence in medical imaging: A comprehensive guide to open-access platforms and tools. (March 2021)

• Record Type: Journal Article
• Authors: Diaz, Oliver; Kushibar, Kaisar; Osuala, Richard; Linardos, Akis; Garrucho, Lidia; Igual, Laura; Radeva, Petia; Prior, Fred; Gkontra, Polyxeni; Lekadir, Karim
• Is Part Of: Physica medica. Volume 83(2021)
• Page Start: 25
• Page End: 37
• Publication Date: 2021-03
• Subjects: Artificial intelligence -- Medical image datasets -- Data curation -- Annotation -- Segmentation -- Image storage -- Open access -- Image quality assessment; Medical physics -- Periodicals; Biophysics -- Periodicals; Biophysics -- Periodicals; Imagerie médicale -- Périodiques; Radiothérapie -- Périodiques; Rayons X -- Sécurité -- Mesures -- Périodiques; Physique -- Périodiques; Médecine -- Périodiques; 610.153
• DOI: 10.1016/j.ejmp.2021.02.007
• Languages: English
• ISSNs: 1120-1797

Abstract

Highlights: Image pre-processing tools are critical to develop and assess AI solutions. Open access tools and data are widely available for medical image preparation. AI needs Big Data to develop and fine-tune a model properly. Big Data needs AI to fully interpret the decision making process. Abstract: The vast amount of data produced by today's medical imaging systems has led medical professionals to turn to novel technologies in order to efficiently handle their data and exploit the rich information present in them. In this context, artificial intelligence (AI) is emerging as one of the most prominent solutions, promising to revolutionise every day clinical practice and medical research. The pillar supporting the development of reliable and robust AI algorithms is the appropriate preparation of the medical images to be used by the AI-driven solutions. Here, we provide a comprehensive guide for the necessary steps to prepare medical images prior to developing or applying AI algorithms. The main steps involved in a typical medical image preparation pipeline include: (i) image acquisition at clinical sites, (ii) image de-identification to remove personal information and protect patient privacy, (iii) data curation to control for image and associated information quality, (iv) image storage, and (v) image annotation. There exists a plethora of open access tools to perform each of the aforementioned tasks and are hereby reviewed. Furthermore, we detail medical image repositories covering different organs and diseases. Such repositories are constantly increasing and enriched with the advent of big data. Lastly, we offer directions for future work in this rapidly evolving field.