T-SYNTH: A Knowledge-Based Dataset of Synthetic Breast Images
This addresses data scarcity for researchers and developers in medical imaging, particularly for breast cancer detection, but is incremental as it builds on existing synthetic data methods.
The paper tackles the problem of limited annotated medical imaging datasets by generating synthetic breast images using physics simulations, resulting in T-SYNTH, a large-scale open-source dataset of paired 2D and 3D images that shows promise for augmenting real datasets in detection tasks.
One of the key impediments for developing and assessing robust medical imaging algorithms is limited access to large-scale datasets with suitable annotations. Synthetic data generated with plausible physical and biological constraints may address some of these data limitations. We propose the use of physics simulations to generate synthetic images with pixel-level segmentation annotations, which are notoriously difficult to obtain. Specifically, we apply this approach to breast imaging analysis and release T-SYNTH, a large-scale open-source dataset of paired 2D digital mammography (DM) and 3D digital breast tomosynthesis (DBT) images. Our initial experimental results indicate that T-SYNTH images show promise for augmenting limited real patient datasets for detection tasks in DM and DBT. Our data and code are publicly available at https://github.com/DIDSR/tsynth-release.