T-SYNTH: A Knowledge-Based Dataset of Synthetic Breast Images
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arXiv
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| Autori principali: | , , , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2025
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| _version_ | 1866912592394452992 |
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| author | Wiedeman, Christopher Sarmakeeva, Anastasiia Sizikova, Elena Filienko, Daniil Lago, Miguel Delfino, Jana G. Badano, Aldo |
| author_facet | Wiedeman, Christopher Sarmakeeva, Anastasiia Sizikova, Elena Filienko, Daniil Lago, Miguel Delfino, Jana G. Badano, Aldo |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2507_04038 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | T-SYNTH: A Knowledge-Based Dataset of Synthetic Breast Images Wiedeman, Christopher Sarmakeeva, Anastasiia Sizikova, Elena Filienko, Daniil Lago, Miguel Delfino, Jana G. Badano, Aldo Computer Vision and Pattern Recognition Artificial Intelligence 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. |
| title | T-SYNTH: A Knowledge-Based Dataset of Synthetic Breast Images |
| topic | Computer Vision and Pattern Recognition Artificial Intelligence |
| url | https://arxiv.org/abs/2507.04038 |