Image annotation and curation in radiology: an overview for machine learning practitioners.
Eur Radiol Exp
; 8(1): 11, 2024 Feb 06.
Article
in En
| MEDLINE
| ID: mdl-38316659
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.
Key words
Full text:
1
Collection:
01-internacional
Database:
MEDLINE
Main subject:
Radiology
/
Artificial Intelligence
Limits:
Humans
Country/Region as subject:
America do norte
Language:
En
Journal:
Eur Radiol Exp
Year:
2024
Document type:
Article
Affiliation country:
Switzerland
Publication country:
ENGLAND
/
ESCOCIA
/
GB
/
GREAT BRITAIN
/
INGLATERRA
/
REINO UNIDO
/
SCOTLAND
/
UK
/
UNITED KINGDOM