BodyMaps/AbdomenAtlas1.1Mini

AbdomenAtlas数据集是迄今为止最大的、完全注释的腹部CT数据集，包含9,262个CT体积，标注了25个不同的解剖结构。该数据集用于Touchstone基准测试项目的第二轮，旨在比较不同的语义分割和预训练算法。

The AbdomenAtlas dataset is the largest, fully-annotated abdominal CT dataset to date, including 9,262 CT volumes with annotations for 25 different anatomical structures. This dataset is used for the second round of the Touchstone Benchmarking Project, which aims to compare diverse semantic segmentation and pre-training algorithms.

原始信息汇总

数据集概述

该数据集是目前最大的完全标注的腹部CT数据集，包含9,262个CT卷，标注了25种不同的解剖结构。

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下载说明

1. 安装Hugging Face库

bash pip install huggingface_hub[hf_transfer]==0.24.0

2. 下载数据集

bash mkdir AbdomenAtlas cd AbdomenAtlas HF_HUB_ENABLE_HF_TRANSFER=1 huggingface-cli download BodyMaps/AbdomenAtlas1.1Mini --repo-type dataset --local-dir .

[可选] 恢复下载

如果下载中断，可以重新运行上述命令。也可以使用以下代码自动重启下载： bash while ! huggingface-cli download BodyMaps/AbdomenAtlas1.1Mini --repo-type dataset --local-dir .; do echo "Resume download failed, waiting for 1 min before retrying..." sleep 60 done

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论文

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 Zhou^1,*
¹Johns Hopkins University,
²Rutgers University,
³City University of Hong Kong,
⁴NVIDIA
NeurIPS 2023
paper | code | dataset | annotation | poster

How Well Do Supervised 3D Models Transfer to Medical Imaging Tasks?
Wenxuan Li, Alan Yuille, and Zongwei Zhou^*
Johns Hopkins University
International Conference on Learning Representations (ICLR) 2024 (oral; top 1.2%)
paper | code

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引用

@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} }