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--- |
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title: Pelvic Bone Fragments with Injuries Segmentation Challenge |
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description: Pelvic fracture segmentation challenge for CT scans and synthetic X-ray images |
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authors: |
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- name: Minh Toan Dinh |
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email: mtoan65@proton.me |
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github: mtoan65 |
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orcid: 0009-0000-3405-4638 |
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version: 1.0.0 |
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license: cc-by-4.0 |
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dataset: |
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- name: PENGWIN Task 1 |
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description: Pelvic Fracture Segmentation on CT |
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doi: 10.5281/zenodo.10927452 |
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- name: PENGWIN Task 2 |
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description: Pelvic Fragment Segmentation on Synthetic X-ray Images |
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doi: 10.5281/zenodo.10913196 |
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tags: |
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- medical-imaging |
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- segmentation |
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- pelvic-fracture |
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- CT |
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- X-ray |
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--- |
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# 𦴠Pelvic Bone Fragments with Injuries Segmentation Challenge π₯ |
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## π Welcome to the [PENGWIN segmentation challenge](https://pengwin.grand-challenge.org/)! |
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<div align="justify"> |
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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. |
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</div> |
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## π Challenge Overview |
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<div align="justify"> |
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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. |
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</div> |
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The PENGWIN segmentation challenge consists of two main tasks: |
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1. **[Task 1: Pelvic fragment segmentation on 3D CT](./Raw/Task_01/README.MD)** |
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- Segment pelvic fractures in 3D CT scans |
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- Dataset: 150 CT scans from diverse patient cohorts |
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2. **[Task 2: Pelvic fragment segmentation on 2D X-ray](./Raw/Task_02/README.MD)** |
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- Segment pelvic fragments in 2D synthetic X-ray images |
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- Dataset: 50,000 synthetic X-ray images derived from 100 CT scans |
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## ποΈ Repository Structure |
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This repository is organized as follows: |
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``` |
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./ |
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βββ assets/ |
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β βββ PENGWIN_banner_vp9y9n3.x10.jpeg |
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β βββ task_1.1.jpg |
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β βββ task_1.2.jpg |
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β βββ task_2.1.png |
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β βββ task_2.2.png |
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βββ Raw/ |
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β βββ Task_01/ # Task 1 dataset and utilities |
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β β βββ PENGWIN_CT_train_images_part1.zip |
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β β βββ PENGWIN_CT_train_images_part2.zip |
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β β βββ PENGWIN_CT_train_labels.zip |
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β β βββ README.MD # Detailed information about Task 1 |
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β βββ Task_02/ # Task 2 dataset and utilities |
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β βββ archive_subfolders.sh |
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β βββ pengwin_utils.py |
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β βββ README.MD # Detailed information about Task 2 |
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β βββ requirements.txt |
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β βββ train/ |
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β βββ input/ |
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β β βββ images/ |
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β β βββ x-ray/ |
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β β βββ 001-010.tar.gz |
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β β βββ 011-020.tar.gz |
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β β βββ ... |
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β βββ output/ |
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β βββ images/ |
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β βββ x-ray/ |
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β βββ 001-010.tar.gz |
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β βββ 011-020.tar.gz |
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β βββ ... |
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βββ README.md # This file |
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``` |
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## π Getting Started |
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To participate in the PENGWIN challenge π: |
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1. **π₯ Download the dataset** from the provided Zenodo links or follow the steps below: |
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1. **π§ Setup Git LFS**: |
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```sh |
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curl -s https://packagecloud.io/install/repositories/github/git-lfs/script.deb.sh | sudo bash |
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sudo apt-get install git-lfs |
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``` |
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2. **π Create and navigate to the Dataset directory**: |
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```sh |
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mkdir Pelvic_Bone_Fragments_with_Injuries_Segmentation_Challenge |
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cd ./Pelvic_Bone_Fragments_with_Injuries_Segmentation_Challenge |
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``` |
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3. **π Initialize Git and add the repository**: |
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```sh |
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git init |
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git remote add origin https://mtoan65:<HF_token>@huggingface.co/datasets/mtoan65/Pelvic_Bone_Fragments_with_Injuries_Segmentation_Challenge |
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``` |
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4. **βοΈ Install Git LFS hook for the repository**: |
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```sh |
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git lfs install |
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``` |
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5. **β¬οΈ Pull the repository**: |
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```sh |
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git checkout -b main |
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git pull origin main |
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``` |
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2. **π Choose the task you want to work on** (Task 1, Task 2, or both). |
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3. **π Follow the instructions in the respective README files**: |
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- [Task 1 README](./Raw/Task_01/README.MD) |
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- [Task 2 README](./Raw/Task_02/README.MD) |
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4. **π¦ Install the required dependencies for each task.** |
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5. **π Start developing your segmentation algorithms!** |
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## π Citation |
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If you use the PENGWIN datasets or challenge in your research, please cite the following: |
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For Task 1: |
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```bibtex |
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@dataset{sang_yudi_2024_10927452, |
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author = {Sang, Yudi and |
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Liu, Yanzhen and |
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Yibulayimu, Sutuke and |
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Zhu, Gang and |
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Wang, Yu and |
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Killeen, Benjamin and |
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Liu, Mingxu and |
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Ku, Ping-Cheng and |
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Armand, Mehran and |
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Unberath, Mathias and |
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Wu, Xinbao and |
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Zhao, Chunpeng}, |
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title = {{PENGWIN Task 1: Pelvic Fracture Segmentation on |
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CT}}, |
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month = apr, |
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year = 2024, |
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publisher = {Zenodo}, |
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version = {v1}, |
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doi = {10.5281/zenodo.10927452}, |
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url = {https://doi.org/10.5281/zenodo.10927452} |
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} |
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``` |
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For Task 2: |
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```bibtex |
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@dataset{killeen_benjamin_2024_10913196, |
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author = {Killeen, Benjamin and |
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Liu, Mingxu and |
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Ku, Ping-Cheng and |
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Yudi, Sang and |
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Liu, Yanzhen and |
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Yibulayimu, Sutuke and |
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Zhu, Gang and |
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Wu, Xinbao and |
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Zhao, Chunpeng and |
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Wang, Yu and |
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Armand, Mehran and |
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Unberath, Mathias}, |
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title = {{PENGWIN Task 2: Pelvic Fragment Segmentation on |
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Synthetic X-ray Images}}, |
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month = apr, |
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year = 2024, |
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publisher = {Zenodo}, |
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version = {1.0.0}, |
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doi = {10.5281/zenodo.10913196}, |
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url = {https://doi.org/10.5281/zenodo.10913196} |
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} |
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``` |
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## π Additional Information |
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For more details about the PENGWIN challenge, please visit the [official challenge webpage](https://grand-challenge.org). |
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## π License |
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The PENGWIN datasets are distributed under the Creative Commons Attribution 4.0 International License. |
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--- |
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## π€ About me |
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<div align="center"> |
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[](https://github.com/mtoan65) |
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[](mailto:mtoan65@proton.me) |
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[](https://www.linkedin.com/in/mtoan65/) |
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[](https://x.com/mtoan65) |
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[](https://huggingface.co/mtoan65) |
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[](https://github.com/mtoan65) |
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[](https://discordapp.com/users/mtoan65#7866) |
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[](https://orcid.org/0009-0000-3405-4638) |
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</div> |