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Dataset Growth in Medical Image Analysis Research.

Nahum Kiryati¹, Yuval Landau¹

¹School of Electrical Engineering, Tel Aviv University, Tel Aviv 69978, Israel.

Journal of Imaging

|August 30, 2021

Summary

This summary is machine-generated.

Medical image analysis researchers face data scarcity. Datasets are growing exponentially, with MRI, CT, and fMRI data sizes increasing annually, impacting research standards.

Keywords:

MICCAI conferences dataset size human subjects medical image analysis

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Area of Science:

Medical image analysis
Computational imaging
Data science in healthcare

Background:

Medical image analysis research relies on large datasets.
Researchers often face challenges in accessing sufficient data, termed 'data starved'.
Evolving community standards may necessitate the use of increasingly larger datasets.

Purpose of the Study:

To analyze the growth trends of medical image dataset sizes used in research.
To forecast future dataset size requirements in medical image analysis.
To validate growth predictions with recent conference data.

Main Methods:

Scanned MICCAI conference proceedings (2011-2018) to identify papers using human MRI, CT, or fMRI datasets.
Extracted and analyzed dataset sizes, calculating median and geometric mean growth rates.
Forecasted dataset sizes for MICCAI 2019 and validated with actual data from the conference.

Main Results:

Median dataset sizes increased 3-10 fold between 2011 and 2018.
Exponential growth observed in geometric mean dataset size: 21% (MRI), 24% (CT), 31% (fMRI) annually (Phase I).
MICCAI 2019 data confirmed forecasts, with revised annual growth rates of 27% (MRI), 30% (CT), 32% (fMRI).

Conclusions:

Medical image dataset sizes are growing exponentially, driven by implicit community standards.
Accurate predictions of dataset size growth can be made for future conferences.
These findings have implications for data management and resource allocation in medical imaging research.