https://github.com/hilab-git/word
[MedIA2022]WORD: A large scale dataset, benchmark and clinical applicable study for abdominal organ segmentation from CT image
https://github.com/hilab-git/word
abdominal-organ-dataset annotation-efficient-learning dataset deep-learning medical-image-segmentation
Last synced: 12 months ago
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[MedIA2022]WORD: A large scale dataset, benchmark and clinical applicable study for abdominal organ segmentation from CT image
- Host: GitHub
- URL: https://github.com/hilab-git/word
- Owner: HiLab-git
- License: gpl-3.0
- Created: 2021-10-30T06:02:22.000Z (over 4 years ago)
- Default Branch: main
- Last Pushed: 2024-09-04T05:58:16.000Z (over 1 year ago)
- Last Synced: 2025-03-30T20:03:20.831Z (about 1 year ago)
- Topics: abdominal-organ-dataset, annotation-efficient-learning, dataset, deep-learning, medical-image-segmentation
- Language: Python
- Homepage: https://www.sciencedirect.com/science/article/pii/S1361841522002705
- Size: 48.7 MB
- Stars: 159
- Watchers: 5
- Forks: 19
- Open Issues: 3
-
Metadata Files:
- Readme: README.md
- License: LICENSE
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README
#
[WORD: A large scale dataset, benchmark and clinical applicable study for abdominal organ segmentation from CT image](https://arxiv.org/pdf/2111.02403.pdf)
* [**New**] **We further annotated an open available challenging cases dataset to evaluate the robustness and generalization of deep learning methods. Please check this repo [RAOS](https://github.com/Luoxd1996/RAOS).**
* **The real clinical application and assessment were conducted in this clinical [paper](https://www.sciencedirect.com/science/article/abs/pii/S0360301623005205), the code is [available](https://github.com/Luoxd1996/AbsegNet).**
* This repo provides the codebase and dataset of work **WORD: A large scale dataset, benchmark and clinical applicable study for abdominal organ segmentation from CT image**. Each download requirement will be approved **within two days**.
* Now, we corrected the results of **ESPNet+ KD** in **Table 8** and the dataset descriptions in **Table 1** with red font [Arxiv](https://arxiv.org/pdf/2111.02403.pdf) and [LaTex](https://www.overleaf.com/read/nghhwrbxcmrm).
* Some information about the **WORD** dataset is presented in the following (the LaTex style tables are [here](https://www.overleaf.com/read/nghhwrbxcmrm)):
Fig. 1. An example in the WORD dataset.
Fig. 2. Volume distribution or each organ in the WORD dataset.
Fig. 3. Comparison results of CNN-based and Transformer-based methods.
Fig. 4. User study based on three junior oncologists independently, each of them comes from a different hospital.
# DataSet
* Now, we have removed the download requirement permission, (PWD for BaiduPan is **ABOD**) and the WORD dataset unzip password is **word@uestc**, as we built a new robustness evaluation benchmark, please check this repo [**RAOS**](https://github.com/Luoxd1996/RAOS).
~~Please contact Xiangde (luoxd1996 AT gmail DOT com) for the dataset (**the label of the testing set can be downloaded now [labelTs](https://github.com/HiLab-git/WORD/blob/main/WORD_V0.1.0_labelsTs.zip)**). Two steps are needed to download and access the dataset: **1) using your google email to apply for the download permission ([Goole Driven](https://drive.google.com/drive/folders/16qwlCxH7XtJD9MyPnAbmY4ATxu2mKu67?usp=sharing), [BaiduPan](https://pan.baidu.com/s/1mXUDbUPgKRm_yueXT6E_Kw))**; **2) using your affiliation email to get the unzip password/BaiduPan access code**. We will get back to you within **two days**, **so please don't send them multiple times**. We just handle the **real-name email** and **your email suffix must match your affiliation**. The email should contain the following information:~~
~~Name/Homepage/Google Scholar: (Tell us who you are.)~~
~~Primary Affiliation: (The name of your institution or university, etc.)~~
~~Job Title: (E.g., Professor, Associate Professor, Ph.D., etc.)~~
~~Affiliation Email: (the password will be sent to this email, we just reply to the email which is the end of "edu".)~~
~~How to use: (Only for academic research, not for commercial use or second-development.)~~
# Acknowledgment and Statement
* This dataset belongs to the **Healthcare Intelligence Laboratory** at **University of Electronic Science and Technology of China** and is licensed under the [GNU General Public License v3.0](https://www.gnu.org/licenses/gpl-3.0.html).
* This project has been approved by the privacy and ethical review committee. We thank all collaborators for the data collection, annotation, checking, and user study!
* This project and dataset were designed for **open-available** academic research, **not** for clinical, commercial, second-development, or other use. In addition, if you used it for your academic research, you are encouraged to release the code and the pre-trained model.
* The interesting and memorable name **WORD** is suggested by [Dr. Jie-Neng](https://scholar.google.com/citations?user=yLYj88sAAAAJ&hl=zh-CN), thanks a lot !!!
# Citation
It would be highly appreciated if you cite our paper when using the **WORD** dataset or code:
@article{luo2022word,
title={{WORD}: A large scale dataset, benchmark and clinical applicable study for abdominal organ segmentation from CT image},
author={Xiangde Luo, Wenjun Liao, Jianghong Xiao, Jieneng Chen, Tao Song, Xiaofan Zhang, Kang Li, Dimitris N. Metaxas, Guotai Wang, and Shaoting Zhang},
journal={Medical Image Analysis},
volume={82},
pages={102642},
year={2022},
publisher={Elsevier}}
@article{liao2023comprehensive,
title={Comprehensive Evaluation of a Deep Learning Model for Automatic Organs-at-Risk Segmentation on Heterogeneous Computed Tomography Images for Abdominal Radiation Therapy},
author={Liao, Wenjun and Luo, Xiangde and He, Yuan and Dong, Ye and Li, Churong and Li, Kang and Zhang, Shichuan and Zhang, Shaoting and Wang, Guotai and Xiao, Jianghong},
journal={International Journal of Radiation Oncology* Biology* Physics},
volume={117},
number={4},
pages={994--1006},
year={2023},
publisher={Elsevier}}