Venue: MICCAI 2022 Challenge FLARE

Reviews:

final_score: rating: 8: Top 50% of accepted papers, clear accept
review_content: Pros:
- an adversarial generator-discriminator training pipeline based on EfficientSegNet

Cons:
- lack average used GPU memory 
review_title: Unlabeled Abdominal Multi-organ Image Segmentation Based on Semi-supervised Adversarial Training Strategy

Meta-review: metareview: Reviewer 2:
1. Please include the ORCID for all authors.
2. Please provide the results w/ and w/o unlabeled data on the validation set (50 cases).
3. Please analyze the segmentation efficiency
4. Please adjust the window size and level of CT images to 400/40 in Fig. 5
5. Fig. 4-5 can be zoomed in to fill the space. 

Reviewer 3:
1. The authors should attach ORCID if possible.
2. The segmentation efficiency analysis is missed.
3. Please add the license to your code repo.
4. Please add detailed explanations of the networks in the titles of Figures 1-3

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