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<div align=center> RAOS (dataset&model weight, paper)</div>

Now, the real CT dataset, trained model/log (based on nnunetv1 and 3D-UXNet) and synthetic MRI dataset have been fully released. Please check here RAOS.

Rethinking Abdominal Organ Segmentation (RAOS) in the clinical scenario: A robustness evaluation benchmark with challenging cases.

This dataset consists of 413 real clinical CT scans and 413x9 MR scans, all 19 organs were annotated by a senior oncologist (MD. Wenjun Liao, 10 years experiment).
some challenging cases in clinical practice can help evaluate the generalization and robustness of deep learning methods.
Some organ annotations not present in previous public datasets (such as prostate, seminal vesicles, etc).

<div align=center>Fig. 1. An example in the dataset (one CT and nine synthesized MR scans).<img src="./pictures/data_show.png"></div> <div align=center>Fig. 2. Clinical distribution of the dataset.<img src="./pictures/raos_data_distributation.png"></div>

DataSet

Don't hesitate to contact Xiangde (luoxd1996 AT gmail DOT com) for the dataset. Two steps are needed to download and access the dataset: 1) using your google account to download the data (Goole Driven); 2) using your affiliation email to get the unzip password. 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.)

Citation

It would be highly appreciated if you cite our paper when using this dataset or code:

@article{luo2022word,
  title={{WORD}: A large-scale dataset, benchmark and clinically 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{luo2024rethinking,
  title={Rethinking Abdominal Organ Segmentation (RAOS) in the clinical scenario: A robustness evaluation benchmark with challenging cases},
  author={Luo, Xiangde and Li, Zihan and Zhang, Shaoting and Liao, Wenjun and Wang, Guotai},
  booktitle={Medical Image Computing and Computer Assisted Intervention -- MICCAI 2024},
  year={2024},
  pages={}}

Acknowledgment and Statement

Further details about this work can be found at here.
Please note that a small part of the RAOS dataset is from our previous WORD (where these cases’ annotations are extended from 16 classes to 19 classes), the overlap patients were listed in the overlap_with_word.csv.
The basic information was listed in raos_dataset_clinical_info.xlsx, where the patient did not have an organ annotation means missing organ after surgery (1-19 in men, female in 1-17).
This project will be maintained by Xiangde Luo (from UETSC).