T-SYNTH: A Knowledge-Based Dataset of Synthetic Breast Images
Christopher Wiedeman1
, Anastasiia Sarmakeeva1, Elena Sizikova1, Daniil Filienko1, Miguel Lago1, Jana G. Delfino1, Aldo Bdadano1
1: Office of Science and Engineering Laboratories, Center for Devices and Radiological Health, U.S. Food and Drug Administration, Silver Spring, MD 20993 USA
Publication date: 2025/12/31
https://doi.org/10.59275/j.melba.2025-g444
Abstract
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
Keywords
Digital Breast Tomosynthesis (DBT) · Synthetic Data · Lesion Detection
Bibtex
@article{melba:2025:038:wiedeman,
title = "T-SYNTH: A Knowledge-Based Dataset of Synthetic Breast Images",
author = "Wiedeman, Christopher and Sarmakeeva, Anastasiia and Sizikova, Elena and Filienko, Daniil and Lago, Miguel and Delfino, Jana G. and Bdadano, Aldo",
journal = "Machine Learning for Biomedical Imaging",
volume = "3",
issue = "Special Issue on Open Data at MICCAI 2024–2025",
year = "2025",
pages = "833--847",
issn = "2766-905X",
doi = "https://doi.org/10.59275/j.melba.2025-g444",
url = "https://melba-journal.org/2025:038"
}
RIS
TY - JOUR
AU - Wiedeman, Christopher
AU - Sarmakeeva, Anastasiia
AU - Sizikova, Elena
AU - Filienko, Daniil
AU - Lago, Miguel
AU - Delfino, Jana G.
AU - Bdadano, Aldo
PY - 2025
TI - T-SYNTH: A Knowledge-Based Dataset of Synthetic Breast Images
T2 - Machine Learning for Biomedical Imaging
VL - 3
IS - Special Issue on Open Data at MICCAI 2024–2025
SP - 833
EP - 847
SN - 2766-905X
DO - https://doi.org/10.59275/j.melba.2025-g444
UR - https://melba-journal.org/2025:038
ER -