README.md

---
license: cc0-1.0
task_categories:
- image-classification
- image-segmentation
tags:
- medical
pretty_name: M-SYNTH
size_categories:
- 10K<n<100K
---
# M-SYNTH

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

M-SYNTH is a synthetic digital mammography (DM) 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 radiodensity of the mass to that of fibroglandular tissue)
* Relative dose: 20%, 40%, 60%, 80%, 100% of the clinically recommended dose for each density

<p align="center">
<img src='https://raw.githubusercontent.com/DIDSR/msynth-release/main/images/examples.png' width='700'>
</p>

### Dataset Description

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

- **Curated by:** [Elena Sizikova](https://esizikova.github.io/), [Niloufar Saharkhiz](https://www.linkedin.com/in/niloufar-saharkhiz/), [Diksha Sharma](https://www.linkedin.com/in/diksha-sharma-6059977/), [Miguel Lago](https://www.linkedin.com/in/milaan/), [Berkman Sahiner](https://www.linkedin.com/in/berkman-sahiner-6aa9a919/), [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)

### Dataset Sources

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

- **Code:** [https://github.com/DIDSR/msynth-release](https://github.com/DIDSR/msynth-release)
- **Paper:** [https://neurips.cc/virtual/2023/poster/73701](https://neurips.cc/virtual/2023/poster/73701) 
- **Demo:** [https://github.com/DIDSR/msynth-release/tree/master/examples](https://github.com/DIDSR/msynth-release/tree/master/examples)

## Uses

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

M-SYNTH is intended to facilitate testing of AI with pre-computed synthetic mammography data. 

### Direct Use

<!-- This section describes suitable use cases for the dataset. -->

M-SYNTH can be used to evaluate the effect of mass size and density, breast density, and dose on AI performance in lesion detection. 
M-SYNTH can be used to either train or test  pre-trained AI models. 

### Out-of-Scope Use

<!-- This section addresses misuse, malicious use, and uses that the dataset will not work well for. -->

M-SYNTH cannot be used in lieu of real patient examples to make performance determinations.


## 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. -->

M-SYNTH is organized into a directory structure that indicates the parameters. The folder
```
device_data_VICTREPhantoms_spic_[LESION_DENSITY]/[DOSE]/[BREAST_DENSITY]/2/[LESION_SIZE]/SIM/P2_[LESION_SIZE]_[BREAST_DENSITY].8337609.[PHANTOM_FILE_ID]/[PHANTOM_FILEID]/
```
contains image files imaged with the specified parameters. Note that only examples with odd PHANTOM_FILEID contain lesions, others do not.


```
$ tree data/device_data_VICTREPhantoms_spic_1.0/1.02e10/hetero/2/5.0/SIM/P2_5.0_hetero.8337609.1/1/

data/device_data_VICTREPhantoms_spic_1.0/1.02e10/hetero/2/5.0/SIM/P2_5.0_hetero.8337609.1/1/
├── DICOM_dm
│   └── 000.dcm
├── projection_DM1.loc
├── projection_DM1.mhd
└── projection_DM1.raw
```
Each folder contains mammogram data  that can be read from .raw format (.mhd contains supporting data), or DICOM (.dcm) format. 
Coordinates of lesions can be found in .loc files. Segmentations are stored in .raw format and can be found in data/segmentation_masks/* .

See [Github](https://github.com/DIDSR/msynth-release/tree/main/code) for examples of how to access the files, and [examples](https://github.com/DIDSR/msynth-release/tree/main/examples) for code to load each type of file. 
## Bias, Risks, and Limitations

<!-- This section is meant to convey both technical and sociotechnical limitations. -->

Simulation-based testing is constrained to the parameter variability represented in the object model and the acquisition system. 
There is a risk of misjudging model performance if the simulated examples do not capture the variability in real patients. Please
see the paper for a full discussion of biases, risks, and limitations.


## Citation
```
@article{sizikova2023knowledge,
  title={Knowledge-based in silico models and dataset for the comparative evaluation of mammography AI for a range of breast characteristics, lesion conspicuities and doses},
  author={Sizikova, Elena and Saharkhiz, Niloufar and Sharma, Diksha and Lago, Miguel and Sahiner, Berkman and Delfino, Jana G. and Badano, Aldo},
  journal={Advances in Neural Information Processing Systems},
  volume={},
  pages={},
  year={2023}
}
```

## Related Links
1. [Virtual Imaging Clinical Trial for Regulatory Evaluation (VICTRE)](https://www.fda.gov/medical-devices/science-and-research-medical-devices/victre-silico-breast-imaging-pipeline).
2. [FDA Catalog of Regulatory Science Tools to Help Assess New Medical Device](https://www.fda.gov/medical-devices/science-and-research-medical-devices/catalog-regulatory-science-tools-help-assess-new-medical-devices).
3. A. Badano, C. G. Graff, A. Badal, D. Sharma, R. Zeng, F. W. Samuelson, S. Glick, K. J. Myers. [Evaluation of Digital Breast Tomosynthesis as Replacement of Full-Field Digital Mammography Using an In Silico Imaging Trial](http://dx.doi.org/10.1001/jamanetworkopen.2018.5474). JAMA Network Open 2018.
4. A. Badano, M. Lago, E. Sizikova, J. G. Delfino, S. Guan, M. A. Anastasio, B. Sahiner. [The stochastic digital human is now enrolling for in silico imaging trials—methods and tools for generating digital cohorts.](http://dx.doi.org/10.1088/2516-1091/ad04c0) Progress in Biomedical Engineering 2023.   
5. E. Sizikova, N. Saharkhiz, D. Sharma, M. Lago, B. Sahiner, J. G. Delfino, A. Badano. [Knowledge-based in silico models and dataset for the comparative evaluation of mammography AI](https://github.com/DIDSR/msynth-release). NeurIPS 2023 Workshop on Synthetic Data Generation with Generative AI.