Image annotation and curation in radiology: an overview for machine learning practitioners
Abstract “Garbage in, garbage out” summarises well the importance of high-quality data in machine learning and artificial intelligence. All data used to train and validate models should indeed be consistent, standardised, traceable, correctly annotated, and de-identified, considering local regulatio...
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| Format: | Article |
| Language: | English |
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SpringerOpen
2024-02-01
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| Series: | European Radiology Experimental |
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| Online Access: | https://doi.org/10.1186/s41747-023-00408-y |
| _version_ | 1797275907725983744 |
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| author | Fabio Galbusera Andrea Cina |
| author_facet | Fabio Galbusera Andrea Cina |
| author_sort | Fabio Galbusera |
| collection | DOAJ |
| description | Abstract “Garbage in, garbage out” summarises well the importance of high-quality data in machine learning and artificial intelligence. All data used to train and validate models should indeed be consistent, standardised, traceable, correctly annotated, and de-identified, considering local regulations. This narrative review presents a summary of the techniques that are used to ensure that all these requirements are fulfilled, with special emphasis on radiological imaging and freely available software solutions that can be directly employed by the interested researcher. Topics discussed include key imaging concepts, such as image resolution and pixel depth; file formats for medical image data storage; free software solutions for medical image processing; anonymisation and pseudonymisation to protect patient privacy, including compliance with regulations such as the Regulation (EU) 2016/679 “General Data Protection Regulation” (GDPR) and the 1996 United States Act of Congress “Health Insurance Portability and Accountability Act” (HIPAA); methods to eliminate patient-identifying features within images, like facial structures; free and commercial tools for image annotation; and techniques for data harmonisation and normalisation. Relevance statement This review provides an overview of the methods and tools that can be used to ensure high-quality data for machine learning and artificial intelligence applications in radiology. Key points • High-quality datasets are essential for reliable artificial intelligence algorithms in medical imaging. • Software tools like ImageJ and 3D Slicer aid in processing medical images for AI research. • Anonymisation techniques protect patient privacy during dataset preparation. • Machine learning models can accelerate image annotation, enhancing efficiency and accuracy. • Data curation ensures dataset integrity, compliance, and quality for artificial intelligence development. Graphical Abstract |
| first_indexed | 2024-03-07T15:20:45Z |
| format | Article |
| id | doaj.art-36d6f3ed59d3482f853d3b166b2812c2 |
| institution | Directory Open Access Journal |
| issn | 2509-9280 |
| language | English |
| last_indexed | 2024-03-07T15:20:45Z |
| publishDate | 2024-02-01 |
| publisher | SpringerOpen |
| record_format | Article |
| series | European Radiology Experimental |
| spelling | doaj.art-36d6f3ed59d3482f853d3b166b2812c22024-03-05T17:38:14ZengSpringerOpenEuropean Radiology Experimental2509-92802024-02-018111210.1186/s41747-023-00408-yImage annotation and curation in radiology: an overview for machine learning practitionersFabio Galbusera0Andrea Cina1Spine Center, Schulthess ClinicSpine Center, Schulthess ClinicAbstract “Garbage in, garbage out” summarises well the importance of high-quality data in machine learning and artificial intelligence. All data used to train and validate models should indeed be consistent, standardised, traceable, correctly annotated, and de-identified, considering local regulations. This narrative review presents a summary of the techniques that are used to ensure that all these requirements are fulfilled, with special emphasis on radiological imaging and freely available software solutions that can be directly employed by the interested researcher. Topics discussed include key imaging concepts, such as image resolution and pixel depth; file formats for medical image data storage; free software solutions for medical image processing; anonymisation and pseudonymisation to protect patient privacy, including compliance with regulations such as the Regulation (EU) 2016/679 “General Data Protection Regulation” (GDPR) and the 1996 United States Act of Congress “Health Insurance Portability and Accountability Act” (HIPAA); methods to eliminate patient-identifying features within images, like facial structures; free and commercial tools for image annotation; and techniques for data harmonisation and normalisation. Relevance statement This review provides an overview of the methods and tools that can be used to ensure high-quality data for machine learning and artificial intelligence applications in radiology. Key points • High-quality datasets are essential for reliable artificial intelligence algorithms in medical imaging. • Software tools like ImageJ and 3D Slicer aid in processing medical images for AI research. • Anonymisation techniques protect patient privacy during dataset preparation. • Machine learning models can accelerate image annotation, enhancing efficiency and accuracy. • Data curation ensures dataset integrity, compliance, and quality for artificial intelligence development. Graphical Abstracthttps://doi.org/10.1186/s41747-023-00408-yArtificial intelligenceData curationImage processing (computer-assisted)Machine learningPrivacy |
| spellingShingle | Fabio Galbusera Andrea Cina Image annotation and curation in radiology: an overview for machine learning practitioners European Radiology Experimental Artificial intelligence Data curation Image processing (computer-assisted) Machine learning Privacy |
| title | Image annotation and curation in radiology: an overview for machine learning practitioners |
| title_full | Image annotation and curation in radiology: an overview for machine learning practitioners |
| title_fullStr | Image annotation and curation in radiology: an overview for machine learning practitioners |
| title_full_unstemmed | Image annotation and curation in radiology: an overview for machine learning practitioners |
| title_short | Image annotation and curation in radiology: an overview for machine learning practitioners |
| title_sort | image annotation and curation in radiology an overview for machine learning practitioners |
| topic | Artificial intelligence Data curation Image processing (computer-assisted) Machine learning Privacy |
| url | https://doi.org/10.1186/s41747-023-00408-y |
| work_keys_str_mv | AT fabiogalbusera imageannotationandcurationinradiologyanoverviewformachinelearningpractitioners AT andreacina imageannotationandcurationinradiologyanoverviewformachinelearningpractitioners |