The challenge submission is based on Docker container. So, participants should demonstrate basic segmentation skills and the ability to encapsulate their methods in Docker. We provide a playground for participants to practice. Participants should

• Develop any segmentation method (e.g., U-Net) based on the playground training dast and encapsulate the method by Docker.
• Use Docker to predict the testing set and record 5-10 minutes of the predicting process as a video (mp4 format).
• Submit the segmentation results here and upload your Docker to DockerHub.
  (1) docker hub link;
  (2) download link to the recorded inference mp4 video;
  (3) the screenshot of your playground leaderboard results (Mean DSC>0.8).

There are three datasets used in our challenge. First, the training dataset, ImageNetCT-9K, is provided on Google Drive where you can download the ground-truth masks and follow the corresponding instructions to download the CT scans. Note that you can use external datasets to train your model for better performance. Second, the validation dataset, TotalSegmentor, is a public dataset that is used in an instant and continuously opened submission-and-validation leaderboard to evaluate your model repetitively. Last, the private testing dataset, JHH-1K, reflects a real-world, diverse patient population that encompasses a broad spectrum of pathological conditions, age groups, and demographic backgrounds.

After you finish the registration in the first step, the download link to ImageNetCT-9K will be sent to you along with the Entry Number. As for the validation dataset, please follow the instructions to download TotalSegmentor.

The task is to develop a model that can predict high-quality segmentations for abdominal organs. The training data consists of several thousands of examples on which models can be trained and validated.

Teams are allowed to use other data in addition to the official training set in order to construct their models, however, that data must be publicly available as of 1/16/2024. The same applies to pre-trained weights -- they must have been publicly available before the ImageNetCT-9K dataset was released on 1/16. This is to prevent unfair advantages for teams that may have amassed large private datasets. All external data use must be described in detail in each team's accompanying paper (described in the following section).

Based on the performance we achieved by directly training the model on ImageNetCT-9K and evaluated on TotalSegmentor (without post-processing), your model’s performance should be higher than the results in the following table:

The table will be available soon.

Wondering where to start? Some useful tutorials and previous methods are given below:

• Preprocessing: intensity normalization, resampling...
• Extensive data augmentations;
• Coarse-to-fine (two-stage or cascaded) framework;
• Postprocessing: connected component analysis;

• Whole-volume based input;
• Lightweight network modules: residual block with bottleneck, separated convolutions, and pyramid pooling;
• Accelerate inference by ONNX Runtime, TensorRT...

• Using a Larger Patch Size
• Utilizing a Pre-trained Model based on the Totalsegmentor Dataset
• Incorporating Other Publicly Available Data for Training the Same Task

​​The primary goal of challenges like KiTS is to objectively assess the performance of competing methods. This is only possible if teams provide a complete description of the methods they use. Teams should follow the provided template [Overleaf, Google Docs] and provide satisfactory answers to every field. Papers should otherwise follow the ISBI main conference guidelines for paper formatting. Drafts of these papers must be submitted by 04/15/2024.

Due to a short period of development time, we accept docker testing submissions on JHH-1k and segmentation results validation submissions on TotalSegmentator at the beginning. The results of the TotalSegmentator will be presented on the Results page and the ones of JHH-1K will be evaluated locally by the docker container and presented on the Testing Results page.

All teams can directly submit the segmentation results on the challenge page to get the segmentation accuracy metrics. Please compress your results by running "zip XXX.zip ./*.nii.gz" and upload the zip file.

The submission should include: (Email subject: YourTeamName-TeamLeaderName-Testing Submission)

(1) a download link to your Docker container (teamname.tar.gz); If the Docker container does not work, we will return back the error information to the participants. Participants with technical failure are allowed to resubmit their algorithms with one extra time. When the evaluation is finished, we will return back the evaluation metrics via email. All valid submission results will be reported on the leaderboard.

(2) a sanity test video record (download example: Google drive, Baidu Netdisk) Please test your docker on validation case: XXX.nii.gz, XXX.nii.gz, XXX.nii.gz and record the prediction process. For each case, the running time should be within 60s.

(3) a methodology paper (template) Please carefully read the template and the common issue before writing the manuscript. The evaluation process mainly focuses on the paper's completeness. Don't worry about the low wDSC/wNSD. Since this segmentation task is very challenging, all attempts are worth sharing with readers. We will not reject papers because of low wDSC/wNSD.

The submitted Docker container will be evaluated with the following commands. If the Docker container does not work or the paper does not include all the necessary information to reproduce the method, we will return back the error information and review comments to participants.