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.

Ultimate goal: training a model on limited data that can generalize on infinite data!

@article{MP-COVID-19-SegBenchmark,
  title={Towards Data-Efficient Learning: A Benchmark for COVID-19 CT 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 Cao, Tianjia and Zhu, Yuntao and Nie, Ziwei and Yang, Xiaoping},
  journal = {Medical Physics},
  volume = {48},
  number = {3},
  pages = {1197-1210},
  doi = {https://doi.org/10.1002/mp.14676},
  year = {2021}
}

Datasets

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.

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.

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.

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. 100 pretrained baseline models and corresponding segmentation results are available: 3D U-Net and 2D U-Net.

Baidu Net Disk mirror (pw: t5mj)

• How to reproduce the baseline results?

Step 1. Install the nnU-Net following the official guidance.

Step 2. Download the 3D or 2D trained models and put them into your model folder.

Step 3. Run the inference code.

• Github mirror; Gitee mirror.

Update

• 2020.12: A large COVID-19 CT dataset with 632 patients is available at The Cancer Imaging Archive
• 2020.06.14: Introducing MosMed COVID-19 dataset as an independent testing set for each task and reporting corresponding results.

Due to the license limitation, we can not directly share this dataset, pleanse download it from the official homepage.

• 2020.06.30: Lung annotations of MSD dataset. Baidu NetDisk (pw: q2qv)

TODO

• Provide pretrained 3D U-Net models by 5.6.

• Provide pretrained 2D U-Net models by 5.31.

• Provide lung annotations of MSD dataset by 6.30.

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.