Towards Efficient COVID-19 CT Annotation: A Benchmark for Lung and Infection Segmentation

Task 1: Learning with limited annotations
Task 2: Learning to segment COVID-19 CT scans from non-COVID-19 CT scans
Task 3: Learning with both COVID-19 and non-COVID-19 CT scans

Motivation

Tremendous studies show that deep learning methods have potential for providing accurate and quantitative assessment of COVID-19 infection in CT scans if hundreds of well-labeled training cases are available. However, manual delineation of lung and infection is time-consuming and labor-intensive. Thus, we set up this benchmark to explore annotation-efficient methods for COVID-19 CT scans segmentation. In particular, we focus on learning to segment left lung, right lung and infection using

pure but limited COVID-19 CT scans;
existing labeled lung CT dataset from other non-COVID-19 lung diseases;
heterogeneous datasets include both COVID-19 and non-COVID-19 CT scans.

Datasets

Download Dataset	Description	License
StructSeg 2019	50 lung CT scans; Annotations include left lung, right lung, spinal cord, esophagus, heart, trachea and gross target volume of lung cancer.	Hold by the challenge organizers
NSCLC	402 lung CT scans; Annotations include left lung, right lung and pleural effusion (78 cases).	CC BY-NC
MSD Lung Tumor	63 lung CT scans; Annotations include lung cancer.	CC BY-SA
COVID-19-CT-Seg	20 lung CT scans from; Annotations include left lung, right lung and infections.	CC BY-NC-SA

Segmentation Task 1: Learning with limited annotations

This task is based on the COVID-19-CT-Seg dataset with 20 cases. Three subtasks are to segment lung, infection or both of them. For each task, 5-fold cross-validation results should be reported. It should be noted that each fold only has 4 training cases, and remained 16 cases are used for testing. In other words, this is a few-shot or zero-shot segmentation task. Dataset split file and quantitative results of U-Net baseline are presented in Task1 folder.

Subtask	Training and Testing
Lung	5-fold cross validation 4 cases (20% for training) 16 cases (80% for testing)
Infection
Lung and infection

Segmentation Task 2: Learning to segment COVID-19 CT scans from non-COVID-19 CT scans

This task is to segment lung and infection in COVID-19 CT scans. The main difficulty is that the training set and testing set differ in data distribution. Although all the datasets are lung CT, they vary in lesion types (i.e., cancer, pleural effusion, and COVID-19), patient cohorts and imaging scanners.

It should be noted that labeled COVID-19 CT scans are not allowed to be used during training. The following table presents the details of training, validation, and testing set. Name (Num.) denotes the dataset name and the number of cases in this dataset, e.g., StructSeg Lung (40) denotes that 40 cases in StructSeg dataset are used for training.

Dataset split file and quantitative results of U-Net baseline are presented in Task2 folder.

Subtask	Training	Validation	(Unseen)Testing
Lung	StructSeg Lung (40) NSCLC Lung (322)	StructSeg Lung (10) NSCLC Lung (80)	COVID-19-CT-Seg Lung (20)
Infection	MSD Lung Tumor (51) StructSeg Gross Target (40) NSCLC Plcural Effusion (62)	MSD Lung Tumor (12) StructSeg Gross Target (10) NSCLC Plcural Effusion (16)	COVID-19-CT-Seg Infection(20)

Segmentation Task 3: Learning with both COVID-19 and non-COVID-19 CT scans

This task is also to segment lung and infection in COVID-19 CT scans, but a limited labeled COVID-19 CT scans are allowed to be used during training. For each subtask, 5-fold cross-validation results should be reported.

Dataset split file and quantitative results of U-Net baseline will be presented in Task3 folder.

Subtask	Training		Validation	Testing
Lung	StructSeg Lung (40) NSCLC Lung (322)	COVID-19-CT-Seg Lung(4)	StructSeg Lung (10) NSCLC Lung (80)	COVID-19-CT-Seg Lung(16)
Infection	MSD Lung Tumor (51) StructSeg Gross Target (40) NSCLC Plcural Effusion (62)	COVID-19-CT-Seg Infection(4)	MSD Lung Tumor (12) StructSeg Gross Target (10) NSCLC Plcural Effusion (16)	COVID-19-CT-Seg Infection(16)

Guidelines

We hope these tasks can serve as a benchmark for novel annotation-efficient segmentation methods of COVID-19 CT scans. Both semi-automatic (e.g., level set, graph cut...) and fully automatic methods (e.g., CNNs...) are welcome.
Evaluation metrics are Dice similarity coefficient (DSC) and normalized surface Dice (NSD), and the python implementations are here.
In COVID-19-CT-Seg dataset, the last 10 cases from Radiopaedia have been adjusted to lung window [-1250,250], and then normalized to [0,255], we recommend to adust the first 10 cases from Coronacases with the same method.
Nifty format of the NSCLC dataset can be downloaded here (pw:1qop). It should be noted that all the copyrights belong to the original dataset contributors, and please also cite the corresponding publications if you use this dataset.
2D/3D U-Net baselines are based on nnU-Net.
45 pretrained 3D U-Net baseline models and corresponding segmentation results are available here. Baidu Net Disk mirror (pw: t5mj)
Github mirror; Gitee mirror.

TODO

Provide pretrained 3D U-Net models by 5.6.
Provide pretrained 2D U-Net models by 5.31.

Acknowledgements

We thank all the organizers of MICCAI 2018 Medical Segmentation Decathlon, MICCAI 2019 Automatic Structure Segmentation for Radiotherapy Planning Challenge, the Coronacases Initiative and Radiopaedia for the publicly available lung CT dataset. We also thank Joseph Paul Cohen for providing convenient download link of 20 COVID-19 CT scans. We also thank all the contributor of NSCLC and COVID-19-Seg-CT dataset for providing annotations of lung, pleural effusion and COVID-19 infection. We also thank the organizers of TMI Special Issue on Annotation-Efficient Deep Learning for Medical Imaging because we get lots of insights from the call for papers when designing these segmentation tasks. We also thank the contributors of these great COVID-19 related resources: COVID19_imaging_AI_paper_list and MedSeg. Last but not least, we thank Chen Chen, Xin Yang, and Yao Zhang for their important feedback on this benchmark.

Including the following two citations in your work would be highly appreciated.

Jun Ma, Yixin Wang, Xingle An, Cheng Ge, Ziqi Yu, Jianan Chen, Qiongjie Zhu, Guoqiang Dong, Jian He, Zhiqiang He, Ziwei Nie, Xiaoping Yang, "Towards Efficient COVID-19 CT Annotation: A Benchmark for Lung and Infection Segmentation," arXiv preprint arXiv:2004.12537, 2020

@article{COVID-19-SegBenchmark,
  title={Towards Efficient COVID-19 CT Annotation: A Benchmark for Lung and Infection Segmentation},
  author={Ma Jun and Wang Yixin and An Xingle and Ge Cheng and Yu Ziqi and Chen Jianan and Zhu Qiongjie and Dong Guoqiang and He Jian and He Zhiqiang and Ni Ziwei and Yang Xiaoping},
  journal={arXiv preprint arXiv:2004.12537},
  year={2020}
}

Jun Ma, Cheng Ge, Yixin Wang, Xingle An, Jiantao Gao, Ziqi Yu, Minqing Zhang, Xin Liu, Xueyuan Deng, Shucheng Cao, Hao Wei, Sen Mei, Xiaoyu Yang, Ziwei Nie, Chen Li, Lu Tian, Yuntao Zhu, Qiongjie Zhu, Guoqiang Dong, Jian He. (2020). COVID-19 CT Lung and Infection Segmentation Dataset (Version 1.0) [Data set]. Zenodo. http://doi.org/10.5281/zenodo.3757476

@dataset{COVID-19-CT-Seg-Dataset,
  author       = {Ma Jun and
                  Ge Cheng and
                  Wang Yixin and
                  An Xingle and
                  Gao Jiantao and
                  Yu Ziqi and
                  Zhang Minqing and
                  Liu Xin and
                  Deng Xueyuan and
                  Cao Shucheng and
                  Wei Hao and
                  Mei Sen and
                  Yang Xiaoyu and
                  Nie Ziwei and
                  Li Chen and
                  Tian Lu and
                  Zhu Yuntao and
                  Zhu Qiongjie and
                  Dong Guoqiang and
                  He Jian},
  title        = {{COVID-19 CT Lung and Infection Segmentation 
                   Dataset}},
  month        = Apr,
  year         = 2020,
  publisher    = {Zenodo},
  version      = {1.0},
  doi          = {10.5281/zenodo.3757476},
  url          = {https://doi.org/10.5281/zenodo.3757476}
}

ZiweiNiexiaoer / COVID-19-CT-Seg-Benchmark