README.md

---
license: cc0-1.0
task_categories:
- image-classification
- image-segmentation
tags:
- medical
pretty_name: T-SYNTH
size_categories:
- 1K<n<10K
---
# T-SYNTH

<!-- Provide a quick summary of the dataset. -->

T-SYNTH is a synthetic digital breast tomosynthesis (DBT) dataset with four breast fibroglandular density distributions imaged using Monte Carlo x-ray simulations with the publicly available [Virtual Imaging Clinical Trial for Regulatory Evaluation (VICTRE)](https://github.com/DIDSR/VICTRE) toolkit.

## Dataset Details

The dataset has the following characteristics:

* Breast density: dense, heterogeneously dense, scattered, fatty
* Mass radius (mm): 5.00, 7.00, 9.00
* Mass density: 1.0, 1.06, 1.1 (ratio of mass radiodensity to that of fibroglandular tissue)


### Dataset Description

<!-- Provide a longer summary of what this dataset is. -->

- **Curated by:** [Christopher Wiedeman](https://www.linkedin.com/in/christopher-wiedeman-a0b01014b), [Anastasiia Sarmakeeva](https://www.linkedin.com/in/anastasiia-sarmakeeva/), [Elena Sizikova](https://elenasizikova.github.io/), [Daniil Filienko](https://www.linkedin.com/in/daniil-filienko-800160215/), [Miguel Lago](https://www.linkedin.com/in/milaan/), [Jana Gut Delfino](https://www.linkedin.com/in/janadelfino/), [Aldo Badano](https://www.linkedin.com/in/aldobadano/)
- **License:** Creative Commons 1.0 Universal License (CC0)

## Data Acquisition Details

**Imaging Modality:** Paired 2D digital mammography (DM) and 3D digital breast tomosynthesis (DBT) images. The DBT images are projected into C-VIEW via the method of (Klein, 2023). 

**Manufacturer and Model:** Replica of the Siemens detector based on MC-GPU (Badal and Badano, 2009).

**Demographics:** All breast phantoms are synthetic and do not represent real human subjects.

**Cohort Description:** 9,000 synthetic digital breast tomosynthesis (DBT) samples, distributed across four breast density categories:

| Breast Density        | Fatty     | Scattered | Hetero  | Dense   | **Total** |
| --------- | --------- | --------- | ------- | ------- | --------- |
| **Les.-free / Les.-present** | 1350/1350 | 1350/1350 | 900/900 | 900/900 | 4500/4500 |


**Annotation Protocols:** Lesion masks and bounding boxes were generated automatically from the phantom.

**Data Format and Structure:** Image files are in .raw format.

### Dataset Sources

<!-- Provide the basic links for the dataset. -->

- **Code:** [https://github.com/DIDSR/tsynth-release](https://github.com/DIDSR/tsynth-release)
- **Arxiv:** [https://arxiv.org/abs/2507.04038](https://arxiv.org/abs/2507.04038)

## Intended Use

<!-- Address questions around how the dataset is intended to be used. -->

T-SYNTH is intended to facilitate testing of AI with pre-computed synthetic digital breast tomosynthesis (DBT) data, complementing the M-SYNTH synthetic mammography dataset.


## Ethical Considerations
This work is using synthetically generated data and does not include any real patient-identifiable information. Publication of synthetic data aims to promote transparency, 
reproducibility, and fairness in medical AI research. We recommend avoiding training models only on synthetic data without appropriate validation.


## Dataset Structure

<!-- This section provides a description of the dataset fields, and additional information about the dataset structure such as criteria used to create the splits, relationships between data points, etc. -->

Directory layout:
```
T-SYNTH/data/
├── cview
├── embed_metadata
├── pretrained_models
├── results
└── volumes_subset

```
Descriptions:

* **`cview/`** -- contains T-SYNTH C-VIEW images.

* **`embed_metadata/`** -- Configuration files needed to reproduce experiments.

* **`pretrained_models/`**  -- Pretrained models for ```DBT```, ```DM``` and ```diffusion``` experiments to reproduce results from the paper. Note to reproduce you need files from **`embed_metadata/`**.

* **`results/`** -- Output data and plots used in the paper (see [T-SYNTH repository](https://github.com/DIDSR/tsynth-release/tree/main/code/notebooks)). Description for each experiment could be found [here](https://github.com/DIDSR/tsynth-release/blob/main/code/README.md#experiment-configuration-map).

* **`volumes_subset/`** -- example of volumetric data. The full data set can be downloaded via the following [instructions](https://github.com/DIDSR/tsynth-release/blob/main/code/README.md#optional-download-all-volumes).


The data is organized by lesion size, breast density and lesion density.  Folder names follow the convention:
   ```output_cview_det_Victre/device_data_VICTREPhantoms_spic_[LESION_DENSITY]/[BREAST_DENSITY]/2/[LESION_SIZE]/SIM.zip```.
   
Example path in `volumes_subset`:

```
device_data_VICTREPhantoms_spic_1.1/fatty/2/5.0/SIM/D2_5.0_fatty.1/1/
├── reconstruction1.loc        # Lesion coordinates
├── reconstruction1.mhd        # Metadata (raw format)
├── reconstruction1.raw        # Raw image data
└── reconstruction1_mask.h5    # Pixel-level segmentation masks for lesions and tissues
```

## How to use it

The description how to use it could be found [here](https://github.com/DIDSR/tsynth-release/blob/main/code/README.md). 

## Citation

```
@article{t-synth,
  title={T-SYNTH: A Knowledge-Based Dataset of Synthetic Breast Images},
  author={Christopher Wiedeman, Anastasiia Sarmakeeva, Elena Sizikova, Daniil Filienko, Miguel Lago, Jana G. Delfino, Aldo Badano},
  journal={},
  volume={},
  pages={},
  year={2025}
}
```

## Related Links
1. [Virtual Imaging Clinical Trial for Regulatory Evaluation (VICTRE)](https://cdrh-rst.fda.gov/victre-silico-breast-imaging-pipeline).
2. [M-SYNTH: A Dataset for the Comparative Evaluation of Mammography AI](https://cdrh-rst.fda.gov/m-synth-dataset-comparative-evaluation-mammography-ai).
6. A. Kim*, N. Saharkhiz*, E. Sizikova*, M. Lago, B. Sahiner, J. G. Delfino, A. Badano. [S-SYNTH: Knowledge-Based, Synthetic Generation of Skin Images](https://github.com/DIDSR/ssynth-release). MICCAI 2024.
4. [FDA Catalog of Regulatory Science Tools to Help Assess New Medical Devices](https://www.fda.gov/medical-devices/science-and-research-medical-devices/catalog-regulatory-science-tools-help-assess-new-medical-devices).