The dataset viewer is not available for this dataset.
Cannot get the config names for the dataset.
Error code:   ConfigNamesError
Exception:    DataFilesNotFoundError
Message:      No (supported) data files found in AbdomenAtlas/AbdomenAtlas1.0Mini
Traceback:    Traceback (most recent call last):
                File "/src/services/worker/src/worker/job_runners/dataset/config_names.py", line 73, in compute_config_names_response
                  config_names = get_dataset_config_names(
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/inspect.py", line 347, in get_dataset_config_names
                  dataset_module = dataset_module_factory(
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/load.py", line 1873, in dataset_module_factory
                  raise e1 from None
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/load.py", line 1854, in dataset_module_factory
                  return HubDatasetModuleFactoryWithoutScript(
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/load.py", line 1245, in get_module
                  module_name, default_builder_kwargs = infer_module_for_data_files(
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/load.py", line 595, in infer_module_for_data_files
                  raise DataFilesNotFoundError("No (supported) data files found" + (f" in {path}" if path else ""))
              datasets.exceptions.DataFilesNotFoundError: No (supported) data files found in AbdomenAtlas/AbdomenAtlas1.0Mini

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Dataset Summary

The largest, fully-annotated CT dataset to date, including 5,195 annotated CT volumes (with spleen, liver, kidneys, stomach, gallbladder, pancreas, aorta, and IVC annotations).


Join the AbdomenAtlas Benchmarking Project

The Benchmarking Project aims to compare diverse semantic segmentation and pre-training algorithms. We, the CCVL research group at Johns Hopkins University, invite creators of these algorithms to contribute to the initiative. With our support, contributors will train their methodologies on the largest annotated CT dataset to date. Subsequently, we will evaluate the trained models using a large internal dataset at Johns Hopkins University. Contributors to this large-scale project will be offered authorship in the resulting paper. If you are the creator of a semantic segmentation or pre-training algorithm and wish to advance medical AI by participating in the Benchmark Project, please reach out to pedro.salvadorbassi2@unibo.it.


Downloading Instructions

1- Install the Hugging Face library:

pip install -U "huggingface_hub[cli]"

2- Download the dataset:

mkdir AbdomenAtlas
cd AbdomenAtlas
huggingface-cli download AbdomenAtlas/AbdomenAtlas1.0Mini  --repo-type dataset --local-dir .  --cache-dir ./cache
[Optional] Resume downloading

In case you had a previous interrupted download, resume it by adding “--resume-download” to the download command:

huggingface-cli download AbdomenAtlas/AbdomenAtlas1.0Mini  --repo-type dataset --local-dir .  --cache-dir ./cache --resume-download

Paper

AbdomenAtlas-8K: Annotating 8,000 CT Volumes for Multi-Organ Segmentation in Three Weeks
Chongyu Qu1, Tiezheng Zhang1, Hualin Qiao2, Jie Liu3, Yucheng Tang4, Alan L. Yuille1, and Zongwei Zhou1,*
1 Johns Hopkins University,
2 Rutgers University,
3 City University of Hong Kong,
4 NVIDIA
NeurIPS 2023
paper | code | dataset | annotation | poster

AbdomenAtlas-8K: Human-in-the-Loop Annotating Eight Anatomical Structures for 8,448 Three-Dimensional Computed Tomography Volumes in Three Weeks
Chongyu Qu1, Tiezheng Zhang1, Hualin Qiao2, Jie Liu3, Yucheng Tang4, Alan L. Yuille1, and Zongwei Zhou1,*
1 Johns Hopkins University,
2 Rutgers University,
3 City University of Hong Kong,
4 NVIDIA
RSNA 2023 (Oral Presentation)
paper | code | slides

Citation

@article{qu2023abdomenatlas,
  title={Abdomenatlas-8k: Annotating 8,000 CT volumes for multi-organ segmentation in three weeks},
  author={Qu, Chongyu and Zhang, Tiezheng and Qiao, Hualin and Tang, Yucheng and Yuille, Alan L and Zhou, Zongwei},
  journal={Advances in Neural Information Processing Systems},
  volume={36},
  year={2023}
}

@inproceedings{li2024well,
  title={How Well Do Supervised Models Transfer to 3D Image Segmentation?},
  author={Li, Wenxuan and Yuille, Alan and Zhou, Zongwei},
  booktitle={The Twelfth International Conference on Learning Representations},
  year={2024}
}

Acknowledgements

This work was supported by the Lustgarten Foundation for Pancreatic Cancer Research and partially by the Patrick J. McGovern Foundation Award. We appreciate the effort of the MONAI Team to provide open-source code for the community.

Uploading AbdomenAtlas to HuggingFace

The file AbdomenAtlasUploadMultipleFolders.ipynb has the code we used to upload AbdomenAtlas to Hugging Face. It may be ncessary to run the script multiple times, until it finishes without an uploading error. The uploading script requires PyTorch, huggingface_hub, and Jupyter Notebook.

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