Venue: MICCAI 2022 Challenge FLARE

Reviews:

final_score: rating: 8: Top 50% of accepted papers, clear accept
review_content: The authors give clear and detailed description of their method and experiments.
review_title: Review of MTSegNetSemi-supervised Abdominal Organ Segmentation in CT

final_score: rating: 8: Top 50% of accepted papers, clear accept
review_content: Pros: Unlabeled data is well utilized by using the Mean Teacher Model.

Cons: No statistics for the inference efficiency and resource utilization such as GPU and CPU.
review_title: No statistics for the inference efficiency and resource utilization such as GPU and CPU

final_score: rating: 7: Good paper, accept
review_content: Pros:
1)In this work, they take the mean teacher method to use the unlabeled cases.
2)They study the class-aware weight, preprocessing  and postprocessing influence
3) The final mean DSC is 0.8503.
Cons:
1) talk little about the details of the network.
2) missing the experiment of unlabled cases.
3) It will be better to give the formula of the class-aware weight.
review_title: Good work but talk about the network structure little.

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