RSNA Bone Age 2017 - Dataset Ninja

Introduction #

Safwan S. Halabi, Luciano M. Prevedello, Jayashree Kalpathy-Crameret al.

The dataset for RSNA* Pediatric Bone Age Challenge 2017 was initiated by the authors to showcase the application of machine learning (ML) and artificial intelligence (AI) in the realm of medical imaging. It comprises 14,236 hand radiographs (12,611 in the training set, 1,425 in the validation set, and 200 in the test set) The primary objectives included fostering collaboration for AI model development, leveraging ML techniques to accurately determine skeletal age in pediatric hand radiographs, and identifying innovators in medical imaging.

The dataset, sourced from Children’s Hospital Colorado and Lucile Packard Children’s Hospital at Stanford, included training and validation sets labeled with skeletal boneage (months) estimates and sex. An additional test set was used to evaluate algorithm performance. Ground truth estimates were based on multiple expert reviewers and the Greulich and Pyle standard.

The challenge not only showcased the potential of ML in medical imaging but also highlighted a collaborative and open approach to advancing research in the field. This model, as exemplified by the challenge, is anticipated to inspire future ML challenges, fostering global collaboration and the development of tools to enhance diagnostic capabilities in healthcare.

*RSNA - Radiological Society of North America

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Homepage

Research Paper 1 (main)

Research Paper 2

Research Paper 3

Kaggle

Summary #

RSNA Pediatric Bone Age Challenge 2017 is a dataset for an identification task. It is used in the medical industry.

The dataset consists of 14236 images with 0 labeled objects. There are 3 splits in the dataset: training (12611 images), validation (1425 images), and test (200 images). Alternatively, the dataset could be split into 2 image splits: male (7706 images) and female (6530 images). Additionally, the images are tagged with boneage (months). The dataset was released in 2017 by the Radiological Society of North America.

Explore #

RSNA Bone Age 2017 dataset has 14236 images. Click on one of the examples below or open "Explore" tool anytime you need to view dataset images with annotations. This tool has extended visualization capabilities like zoom, translation, objects table, custom filters and more. Hover the mouse over the images to hide or show annotations.

Sample annotation mask from RSNA Bone Age 2017

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Have a look at 14236 images

View images along with annotations and tags, search and filter by various parameters

License #

You may access and use these de-identified imaging datasets and annotations (“the data”) for the purposes of academic research and education, and other non-commercial purposes as long as you agree to abide by the following provisions:

Not to make any attempt to identify or contact any individual(s) who may be the
subjects of the data.
If you share or re-distribute the data in any form, to meet the attribution requirements
described below:
• Provide a link to download the RSNA Pediatric Bone Age Challenge image datasets and annotation files.
• Include a citation to the 2018 Radiology paper:
Halabi SS, Prevedello LM, Kalpathy-Cramer J, et al. [The RSNA Pediatric Bone Age Machine Learning Challenge](https://pubs.rsna.org/doi/10.1148/radiol.2018180736). Radiology 2018; 290(2):498-503

Source

Citation #

If you make use of the RSNA Bone Age 2017 data, please cite the following reference:

Halabi S, et al. 
The Pediatric Bone Age Machine Learning Challenge.
https://pubs.rsna.org/doi/10.1148/radiol.2018180736

Source

If you are happy with Dataset Ninja and use provided visualizations and tools in your work, please cite us:

@misc{ visualization-tools-for-rsna-bone-age-dataset,
  title = { Visualization Tools for RSNA Bone Age 2017 Dataset },
  type = { Computer Vision Tools },
  author = { Dataset Ninja },
  howpublished = { \url{ https://datasetninja.com/rsna-bone-age } },
  url = { https://datasetninja.com/rsna-bone-age },
  journal = { Dataset Ninja },
  publisher = { Dataset Ninja },
  year = { 2024 },
  month = { jul },
  note = { visited on 2024-07-27 },
}

Download #

Dataset RSNA Bone Age 2017 can be downloaded in Supervisely format:

As an alternative, it can be downloaded with dataset-tools package:

pip install --upgrade dataset-tools

… using following python code:

import dataset_tools as dtools

dtools.download(dataset='RSNA Bone Age 2017', dst_dir='~/dataset-ninja/')

Make sure not to overlook the python code example available on the Supervisely Developer Portal. It will give you a clear idea of how to effortlessly work with the downloaded dataset.

The data in original format can be downloaded here:

. . .

Disclaimer #

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