Venue: MICCAI 2022 Challenge FLARE

Reviews:

final_score: rating: 7: Good paper, accept
review_content: The article uses the nn-UNet-based framework combined with CPS (cross-pseudo-supervised) as the main method. nn-UNet has achieved good results in many medical image segmentation tasks, and it is a good choice to improve on this basis. Similar to the original author of UNet, the author does not focus on improving the network structure. The basic structure of its network is still the base UNet, but a specific preprocessing method is used for specific tasks to calculate the intensity distribution of the data. Secondly, the composite loss function which has been proved is an effective strategy for improving segmentation results. But there are still some areas to be improved:
1. For preprocessing methods different from nn-UNet, there could be a more detailed description
2. Secondly, there is a lack of relevant explanations on whether forced spacing settings strategy in the inference stage to speed up the inference time will cause changes in the segmentation results.
3. Finally, there can be a general description of the possible improvement directions
review_title:  A semi-supervised nnU-Net architecture for abdominal organ segmentation

final_score: rating: 7: Good paper, accept
review_content: Summary:

In this work, the authors propose a new 3D Cross Pseudo Supervision (3D-CPS) method, where the nnU-net architecture first modified for semi-supervised tasks.

Strengths:

The labeled data and unlabeled data are calculated based on cross pseudo supervision during the training process to deal with the imbalance problem in the number of labeled data and unlabeled data.
A force spacing settings strategy is proposed to reduce resources consumption and speed up the inference time.

Weaknesses:

No proper analysis of results on the decrease in the metrics of some organs occurred after the use of additional semi-supervised modules.

Details:

1. The English of your manuscript must be improved before resubmission. Many sentences contain grammatical and/or spelling mistakes or are not complete sentences.
2. The characters in Figure 2, Figure 3, and Figure 4 are very small, please enlarge them appropriately.
3. The strategy of uniform slice spacing is a relatively common pre-processing operation. This approach can't be a contribution.
4. In Table 4 and Table 5, the decrease in the metrics of some organs occurred after the use of additional semi-supervised modules, and it is recommended that the authors appropriately discuss the reasons for the occurrence of this phenomenon.
5. In Section 4.3, considering that this method uses the "enforced spacing settings" strategy and the network input is the resized date, why the sliding window is still needed here?


review_title: 3D Cross Pseudo Supervision (3D-CPS)

final_score: rating: 7: Good paper, accept
review_content: Authors adopt Cross Pseudo Supervision (CPS) for nnUnet, while also propose forced spacing setting to reduce resources consimption.

This is a well-written piece in reviewer's opinion. The tables and figures are logically structured and easy to understand. Qualitative and quantitative results are provided extensively with subjective comments, which is really informative. The preprocessing, postprocessing configuration in their experiments are clearly described.

Suggest improvements:
- Figure 2, backward arrow should be consistent between supervised and unsupervised branch
review_title: This is a well-written piece in reviewer's opinion with extensive experiments and assessment.

Meta-review: metareview: Reviewers raise many concerns and suggestions. Please address all comments in the revised manuscript.
confidence: 5: The area chair is absolutely certain
Decision: Major Revision