Venue: MICCAI 2022 Challenge FLARE

Reviews:

final_score: rating: 9: Top 15% of accepted papers, strong accept
review_content: The method achieves a mean Dice Similarity Coefficient (DSC) of 0.8725 and a Normalized Surface Dice (NSD) of 0.9109 on the FLARE2022 validation dataset, with an average inference time of 15 seconds per case.This is an excellent result.
review_title: Good work!

final_score: rating: 9: Top 15% of accepted papers, strong accept
review_content: Advantages: 1. Great two-phase network with outstanding performance and efficiency. 2. Rational SSL method with performance improvement.
Disadvantages: Fig.5 and typography should be checked.
review_title: Great network design and SSL method

final_score: rating: 8: Top 50% of accepted papers, clear accept
review_content: The authors give detailed description of their method and conduct fully experiments to achieve promising performance.
review_title: Review of Abdominal CT organ segmentation by accelerated nnUNet with a coarse to fine strategy

final_score: rating: 8: Top 50% of accepted papers, clear accept
review_content: Pros: 1. This job used a multi-scale strategy to obtain the pseudo-label and the uncertainty correction enabled a more reliable consistency. This semi-supervised algorithm improved the performance by using the unlabeled data and has been proved by the experimental result.
2. A coarse to fine strategy based on nnUNet has achieved acceleration which shows its consideration for the fast inference.
3. The article has a good presentation of methods and details
Cons:  1. The number of model parameters and flops might not meet the requirements of low resource.
2. The high accuracy mainly come from the pre-processing and coarse-to-fine framework, while the contribution of semi-supervised algorithm was small, denoting that it has not fully use the unlabeled data.

review_title: The article proposes a practical and efficient method based on the proposition of FLARE2022, which achieves a good performance of abdominal segmentation.

final_score: rating: 9: Top 15% of accepted papers, strong accept
review_content: Pros: 1. Interpolation method is optimized based on nnUNet so the inference efficiency is highly accelerated. 2. Unlabeled data is used by semi-supervised training in coarse model which has a ~1% boot in DSC.

Cons: Unlabeled data might also be used for the fine stage model training.
review_title: Unlabeled data might also be used for the fine stage model training

final_score: rating: 9: Top 15% of accepted papers, strong accept
review_content: Pros:
1) This work segment the abdominal organs in a coarse to fine manner and achieving 0.8725 mean DSC.
2) They resampled the data to [3, 2, 2] mm voxel-size for coarse segmentation and [2, 1.5, 1.5] mm for fine segmentation to reduce the resource consume.
3) They also do some optimization for acceleration which reduce the time greatly!


Cons: 
1) They did't show the exactly loss for semi-suprevised learning and unlabeled data.




review_title: Great work and achieve a really fast and fine segmentation!

final_score: rating: 8: Top 50% of accepted papers, clear accept
review_content: Strengths: The proposed method achieves efficient and effective semi-supervised learning with a mean DSC of 0.8725 and a mean inference time of 15 s. Especially, Pytorch-and-GPU-based interpolation for resizing and optimizing the argmax operation dramatically reduced the inference time.

Weaknesses: Efficiency results other than the inference time are not presented explicitly, although Section 2.2 states that the proposed method satisfies the GPU memory requirement of the FLARE22 challenge. 
review_title: Abdominal CT organ segmentation by accelerated nnUNet with a coarse to fine strategy

Meta-review: metareview: Nice paper. Please address the reviewers' comments in the revised manuscript.
confidence: 5: The area chair is absolutely certain
Decision: Minor Revision