---
license: cc-by-2.5
task_categories:
- image-classification
- image-segmentation
language:
- en
tags:
- biology
- X-Ray
size_categories:
- 1K<n<10K
---

# Dataset Card for FracAtlas

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

The "FracAtlas" dataset is a collection of musculoskeletal radiographs for fracture classification, localization, and segmentation. 
It includes 4,083 X-Ray images for bones with annotations in multiple formats.The annotations include labels, classes, and etc. 
The dataset is intended for use in deep learning tasks in medical imaging, specifically targeting the understanding of bone fractures. 
It is freely available under a CC-BY 4.0 license.

### Dataset Description

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- **Curated by:** [More Information Needed]
- **License:** cc-by-2.5

### Dataset Sources

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- **Repository:** https://figshare.com/articles/dataset/The_dataset/22363012
- **Paper:** https://www.nature.com/articles/s41597-023-02432-4

## Uses

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The  "FracAtlas" dataset can be used to develop multiple machine learning or deep learning algorithms. For example: 
1. Developing a deep learning model to automatically detect fractures in radiographs.
2. Classifying the type of fractures (e.g., hairline, compound, transverse) using machine learning models
3. Implementing segmentation models to delineate bone structures from the surrounding tissues in the radiographs
4. Forecasting patients’ outcomes based on the characteristics of the fracture and other patient data
5. Developing models to identify anomalous patterns in the radiographs of bones


## 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. -->

[More Information Needed]

## Dataset Creation

### Curation Rationale

<!-- Motivation for the creation of this dataset. -->

[More Information Needed]

### Source Data

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The data is originally authored by authored by Iftekharul Abedeen, Md. Ashiqur Rahman, Fatema Zohra Prottyasha, Tasnim Ahmed, 
Tareque Mohmud Chowdhury, Swakkhar Shatabda.

#### Data Collection and Processing

<!-- This section describes the data collection and processing process such as data selection criteria, filtering and normalization methods, tools and libraries used, etc. -->

The FracAtlas dataset was accumulatively collected over 14,000 X-ray scans from several medical facilities across Bangladesh, with a substantial portion sourced from Lab-Aid Medical Center. 
Following collection, a meticulous data cleaning phase was undertaken to ensure the integrity and usability of the scans. 
Finally, the dataset was enhanced with detailed annotations. 
Ethical approval was secured, ensuring the confidentiality of patient data, and all participants provided informed consent. 
The collection process was designed to be non-intrusive to the standard diagnostic and treatment protocols of the involved hospitals.


### Annotations

<!-- If the dataset contains annotations which are not part of the initial data collection, use this section to describe them. -->
The dataset includes 4,083 images that have been manually annotated for bone fracture classification, localization, and segmentation with the help of 2 expert radiologists. 
Annotations have later been verified and merged by an orthopedist using the open-source labeling platform, makesense.ai. 
The primary type of annotation generated for bone fracture was in Common Objects in Context (COCO) format. 
Other information were loosely saved in YOLO, Pascal VOC, and Visual Geometry Group (VGG) format.


#### Personal and Sensitive Information

<!-- State whether the dataset contains data that might be considered personal, sensitive, or private (e.g., data that reveals addresses, uniquely identifiable names or aliases, racial or ethnic origins, sexual orientations, religious beliefs, political opinions, financial or health data, etc.). If efforts were made to anonymize the data, describe the anonymization process. -->

All personally identifiable information in the gathered data has been removed, and theprocess was administered according to the Institutional Research Ethics Board of United International University.
## Bias, Risks, and Limitations

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While the FracAtlas dataset is particularly valuable for the development of computer-aided diagnosis systems, its potential limitations should be carefully considered. Firstly, the manual annotation process, is susceptible to human error, which may result in mislabeling. Such inaccuracies can impact the performance of machine learning models trained on this data. 

### Recommendations

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Users should be made aware of the risks, biases and limitations of the dataset. More information needed for further recommendations.

## Citation

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**APA:**

Abedeen, I., Rahman, M. A., Prottyasha, F. Z., Ahmed, T., Chowdhury, T. M., & Shatabda, S. (2023). FracAtlas: A Dataset for Fracture Classification, Localization and Segmentation of Musculoskeletal Radiographs. Scientific data, 10(1), 521. https://doi.org/10.1038/s41597-023-02432-4