--- title: Pelvic Bone Fragments with Injuries Segmentation Challenge description: Pelvic fracture segmentation challenge for CT scans and synthetic X-ray images authors: - name: Minh Toan Dinh email: mtoan65@proton.me github: mtoan65 orcid: 0009-0000-3405-4638 version: 1.0.0 license: cc-by-4.0 dataset: - name: PENGWIN Task 1 description: Pelvic Fracture Segmentation on CT doi: 10.5281/zenodo.10927452 - name: PENGWIN Task 2 description: Pelvic Fragment Segmentation on Synthetic X-ray Images doi: 10.5281/zenodo.10913196 tags: - medical-imaging - segmentation - pelvic-fracture - CT - X-ray --- # ðŸĶī Pelvic Bone Fragments with Injuries Segmentation Challenge ðŸĨ ![PENGWIN Challenge Banner](./assets/PENGWIN_banner_vp9y9n3.x10.jpeg) ## 🌟 Welcome to the [PENGWIN segmentation challenge](https://pengwin.grand-challenge.org/)!
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: 1. **[Task 1: Pelvic fragment segmentation on 3D CT](./Raw/Task_01/README.MD)** - Segment pelvic fractures in 3D CT scans - Dataset: 150 CT scans from diverse patient cohorts 2. **[Task 2: Pelvic fragment segmentation on 2D X-ray](./Raw/Task_02/README.MD)** - Segment pelvic fragments in 2D synthetic X-ray images - Dataset: 50,000 synthetic X-ray images derived from 100 CT scans ## 🗂ïļ 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 🏆: 1. **ðŸ“Ĩ Download the dataset** from the provided Zenodo links or follow the steps below: 1. **🔧 Setup Git LFS**: ```sh curl -s https://packagecloud.io/install/repositories/github/git-lfs/script.deb.sh | sudo bash sudo apt-get install git-lfs ``` 2. **📂 Create and navigate to the Dataset directory**: ```sh mkdir Pelvic_Bone_Fragments_with_Injuries_Segmentation_Challenge cd ./Pelvic_Bone_Fragments_with_Injuries_Segmentation_Challenge ``` 3. **🔗 Initialize Git and add the repository**: ```sh git init git remote add origin https://mtoan65:@huggingface.co/datasets/mtoan65/Pelvic_Bone_Fragments_with_Injuries_Segmentation_Challenge ``` 4. **⚙ïļ Install Git LFS hook for the repository**: ```sh git lfs install ``` 5. **⮇ïļ Pull the repository**: ```sh git checkout -b main git pull origin main ``` 2. **🔍 Choose the task you want to work on** (Task 1, Task 2, or both). 3. **📖 Follow the instructions in the respective README files**: - [Task 1 README](./Raw/Task_01/README.MD) - [Task 2 README](./Raw/Task_02/README.MD) 4. **ðŸ“Ķ Install the required dependencies for each task.** 5. **🚀 Start developing your segmentation algorithms!** ## 📚 Citation If you use the PENGWIN datasets or challenge in your research, please cite the following: For Task 1: ```bibtex @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: ```bibtex @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](https://grand-challenge.org). ## 📄 License The PENGWIN datasets are distributed under the Creative Commons Attribution 4.0 International License. --- ## ðŸ‘Ī About me
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