Venue: MICCAI 2022 Challenge FLARE

Reviews:

final_score: rating: 8: Top 50% of accepted papers, clear accept
review_content: - Good scores
- Nice tables, good visualization of segmentation masks.


Lacks a clear description of the method, the motivation behind student feedback needs more explaining. The image describing the method is quite complicated and hard to understand.

The overall method is good, the presentation not so much. 
review_title: Good paper

final_score: rating: 7: Good paper, accept
review_content: Nice paper, with good DSC scores.

Some comments/suggestions:

- Why do you only validate on 20 cases? The leaderboard shows your performance on 50 cases.
    
- Is pre-processing similar or the same as nnu-net, i.e. are you replacing their pre-processing pipeline? Otherwise it is better to write that it is the same.
    
- You write that you used 1000 unlabelled cases, but there were 2000, why did you not use all?
review_title: Review

Meta-review: metareview: Reviewer 4:

1. Missing description of post-processing.
2. Missing strategies to improve inference speed and reduce resource consumption. If you do not has any design, please explicitly describe that. 
3. Missing limitations and future work.

Reviewer 5:
1. It would be better if the authors could give one or two sentences to describe the "Meta Pseudo Labels" in the abstract.
2. Please provide the results of NSD and efficiency-related metrics in the abstract.
3. Did the authtors leverage any strategy to accelerate the inference procedure?
4. Did the authors exploit any postprocesssing?
5. Please cite "Fast and Low-GPU-memory abdomen CT organ segmentation: The FLARE challenge"
6. Does the Table 4 report the results on the validation set (50 cases)? If not, please provide it.
7. Please discuss the limitation and future work.

Meta review：
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