Dataset Growth in Medical Image Analysis Research

• Published in: Journal of imaging Vol. 7; no. 8; p. 155
• Main Authors: Kiryati, Nahum, Landau, Yuval
• Format: Journal Article
• Language: English
• Published: Switzerland MDPI AG 20.08.2021; MDPI
• Subjects: dataset size; Datasets; Deep learning; Human subjects; Hypotheses; Image analysis; Machine learning; Magnetic resonance imaging; Mean; medical image analysis; Medical imaging; Medical research; MICCAI conferences; Peer review; Statistical analysis; dataset size; human subjects; MICCAI conferences; medical image analysis
• DOI: 10.3390/jimaging7080155
• ISSN: 2313-433X

Medical image analysis research requires medical image datasets. Nevertheless, due to various impediments, researchers have been described as “data starved”. We hypothesize that implicit evolving community standards require researchers to use ever-growing datasets. In Phase I of this research, we scanned the MICCAI (Medical Image Computing and Computer-Assisted Intervention) conference proceedings from 2011 to 2018. We identified 907 papers involving human MRI, CT or fMRI datasets and extracted their sizes. The median dataset size had grown by 3–10 times from 2011 to 2018, depending on imaging modality. Statistical analysis revealed exponential growth of the geometric mean dataset size with an annual growth of 21% for MRI, 24% for CT and 31% for fMRI. Thereupon, we had issued a forecast for dataset sizes in MICCAI 2019 well before the conference. In Phase II of this research, we examined the MICCAI 2019 proceedings and analyzed 308 relevant papers. The MICCAI 2019 statistics compare well with the forecast. The revised annual growth rates of the geometric mean dataset size are 27% for MRI, 30% for CT and 32% for fMRI. We predict the respective dataset sizes in the MICCAI 2020 conference (that we have not yet analyzed) and the future MICCAI 2021 conference.