4.4 Article

Data preparation for artificial intelligence in medical imaging: A comprehensive guide to open-access platforms and tools

期刊

出版社

ELSEVIER SCI LTD
DOI: 10.1016/j.ejmp.2021.02.007

关键词

Artificial intelligence; Medical image datasets; Data curation; Annotation; Segmentation; Image storage; Open access; Image quality assessment

资金

  1. Catalan government [SGR1742]
  2. European Union's Horizon 2020 research and innovation programme [952103]
  3. Spanish Ministry [RTI2018-095232-B-C21]

向作者/读者索取更多资源

The vast amount of data generated by medical imaging systems today has led professionals to explore novel technologies such as artificial intelligence to efficiently handle and utilize the data. Proper preparation of medical images is crucial for the development of reliable AI algorithms, involving steps like image acquisition, de-identification, data curation, storage, and annotation. Open access tools and medical image repositories play important roles in these processes, with future work in this area focusing on further advancements.
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.

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