Datasets:
🦴 Pelvic Bone Fragments with Injuries Segmentation Challenge 🏥
🌟 Welcome to the PENGWIN segmentation challenge!
Pelvic fractures, typically resulting from high-energy traumas, are among the most severe injuries, characterized by a disability rate over 50% and a mortality rate over 13%, ranking them as the deadliest of all compound fractures. The complexity of pelvic anatomy, along with surrounding soft tissues, makes surgical interventions especially challenging. Recent years have seen a shift towards the use of robotic-assisted closed fracture reduction surgeries, which have shown improved surgical outcomes. Accurate segmentation of pelvic fractures is essential, serving as a critical step in trauma diagnosis and image-guided surgery. In 3D CT scans, fracture segmentation is crucial for fracture typing, pre-operative planning for fracture reduction, and screw fixation planning. For 2D X-ray images, segmentation plays a vital role in transferring the surgical plan to the operating room via registration, a key step for precise surgical navigation.
📊 Challenge Overview
As a MICCAI 2024 challenge, the PENGWIN segmentation challenge is designed to advance the development of automated pelvic fracture segmentation techniques in both 3D CT scans (Task 1) and 2D X-ray images (Task 2), aiming to enhance their accuarcy and robustness. Our dataset comprises CT scans from 150 patients scheduled for pelvic reduction surgery, collected from multiple institutions using a variety of scanning equipment. This dataset represents a diverse range of patient cohorts and fracture types. Ground-truth segmentations for sacrum and hipbone fragments have been semi-automatically annotated and subsequently validated by medical experts. Furthermore, we have generated high-quality, realistic X-ray images and corresponding 2D labels from the CT data using the DeepDRR method, incorporating a range of virtual C-arm camera positions and surgical tools.
The PENGWIN segmentation challenge consists of two main tasks:
Task 1: Pelvic fragment segmentation on 3D CT
- Segment pelvic fractures in 3D CT scans
- Dataset: 150 CT scans from diverse patient cohorts
Task 2: Pelvic fragment segmentation on 2D X-ray
- Segment pelvic fragments in 2D synthetic X-ray images
- Dataset: 50,000 synthetic X-ray images derived from 100 CT scans
🗂️ Repository Structure
This repository is organized as follows:
./
├── assets/
│ ├── PENGWIN_banner_vp9y9n3.x10.jpeg
│ ├── task_1.1.jpg
│ ├── task_1.2.jpg
│ ├── task_2.1.png
│ └── task_2.2.png
├── Raw/
│ ├── Task_01/ # Task 1 dataset and utilities
│ │ ├── PENGWIN_CT_train_images_part1.zip
│ │ ├── PENGWIN_CT_train_images_part2.zip
│ │ ├── PENGWIN_CT_train_labels.zip
│ │ └── README.MD # Detailed information about Task 1
│ └── Task_02/ # Task 2 dataset and utilities
│ ├── archive_subfolders.sh
│ ├── pengwin_utils.py
│ ├── README.MD # Detailed information about Task 2
│ ├── requirements.txt
│ └── train/
│ ├── input/
│ │ └── images/
│ │ └── x-ray/
│ │ ├── 001-010.tar.gz
│ │ ├── 011-020.tar.gz
│ │ └── ...
│ └── output/
│ └── images/
│ └── x-ray/
│ ├── 001-010.tar.gz
│ ├── 011-020.tar.gz
│ └── ...
└── README.md # This file
🚀 Getting Started
To participate in the PENGWIN challenge 🏆:
📥 Download the dataset from the provided Zenodo links or follow the steps below:
🔧 Setup Git LFS:
curl -s https://packagecloud.io/install/repositories/github/git-lfs/script.deb.sh | sudo bash sudo apt-get install git-lfs📂 Create and navigate to the Dataset directory:
mkdir Pelvic_Bone_Fragments_with_Injuries_Segmentation_Challenge cd ./Pelvic_Bone_Fragments_with_Injuries_Segmentation_Challenge🔗 Initialize Git and add the repository:
git init git remote add origin https://mtoan65:<HF_token>@huggingface.co/datasets/mtoan65/Pelvic_Bone_Fragments_with_Injuries_Segmentation_Challenge⚙️ Install Git LFS hook for the repository:
git lfs install⬇️ Pull the repository:
git checkout -b main git pull origin main
🔍 Choose the task you want to work on (Task 1, Task 2, or both).
📖 Follow the instructions in the respective README files:
📦 Install the required dependencies for each task.
🚀 Start developing your segmentation algorithms!
📚 Citation
If you use the PENGWIN datasets or challenge in your research, please cite the following:
For Task 1:
@dataset{sang_yudi_2024_10927452,
author = {Sang, Yudi and
Liu, Yanzhen and
Yibulayimu, Sutuke and
Zhu, Gang and
Wang, Yu and
Killeen, Benjamin and
Liu, Mingxu and
Ku, Ping-Cheng and
Armand, Mehran and
Unberath, Mathias and
Wu, Xinbao and
Zhao, Chunpeng},
title = {{PENGWIN Task 1: Pelvic Fracture Segmentation on
CT}},
month = apr,
year = 2024,
publisher = {Zenodo},
version = {v1},
doi = {10.5281/zenodo.10927452},
url = {https://doi.org/10.5281/zenodo.10927452}
}
For Task 2:
@dataset{killeen_benjamin_2024_10913196,
author = {Killeen, Benjamin and
Liu, Mingxu and
Ku, Ping-Cheng and
Yudi, Sang and
Liu, Yanzhen and
Yibulayimu, Sutuke and
Zhu, Gang and
Wu, Xinbao and
Zhao, Chunpeng and
Wang, Yu and
Armand, Mehran and
Unberath, Mathias},
title = {{PENGWIN Task 2: Pelvic Fragment Segmentation on
Synthetic X-ray Images}},
month = apr,
year = 2024,
publisher = {Zenodo},
version = {1.0.0},
doi = {10.5281/zenodo.10913196},
url = {https://doi.org/10.5281/zenodo.10913196}
}
🔗 Additional Information
For more details about the PENGWIN challenge, please visit the official challenge webpage.
📄 License
The PENGWIN datasets are distributed under the Creative Commons Attribution 4.0 International License.
👤 About me
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