Venue: MICCAI 2022 Challenge FLARE

Reviews:

final_score: rating: 8: Top 50% of accepted papers, clear accept
review_content: * Why the best results are not shown in the abstract.
* According to the template, there is no section of segmentation efficiency results and limitation and future work.
review_title: Cross supervision using siamese network 

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

This work proposed SemiSeg-CSSN for semi-supervised multi-organ segmentation and employed UIF to select high-quality unlabeled images. The method achieves an mDSC of 77.7% with low GPU consumption at 12.9s per image.

**Strengths:**

- Complete analysis of FRARE2022 competition train dataset.
- Employ coarse-to-fine network and anisotropic convolution to reduce resource consumption.
- Propose Cross supervision framework and unlabeled image filtering to obtain reliable unlabeled images for semi-supervised training.
- Ablation study for the number of unlabeled data: this experiment shows that UIF effectively selects high-quality unlabeled images.

**Suggest improvements:**

- Add description and figure for network architecture.
- The mDSC of baseline(supervised training) is about 80% or more in this competition, while it's 70.9% in this work. It is recommended to improve the performance of the baseline, and the result will be more credible.
review_title: Well-written paper with solid experiment results

final_score: rating: 8: Top 50% of accepted papers, clear accept
review_content: Nice overview of class imbalance in the introduction. The cross supervision setup is also interesting.

Some comments/suggestions

- Why did you only select 10% of the 2000 cases after uncertainty estimation? It seems like very few.
    
- Figure 6 is really nice
    
- Nice results overall, but I think your baseline performance could be better, for instance using nnU-Net
review_title: Review

final_score: rating: 7: Good paper, accept
review_content: 1. In 2.3, Author said: The siamese networks (Pθ1 and Pθ1 ) have the same structures and parameters but are initialized differently at the beginning of training. I think the sentence should be the networks have the same structures and same number of parameters is better for understanding.

2. In 2.3, Author didn't explain what is $ V_u $.

3. Fig 5, In the figure, the order of coloumn is Intensity Image, SemiSeg-CSSN, Ground truth, but in description the author said the second column is ground truth is kinda confuse.

Over all, This paper is quite clear about author's work but still need refine.

review_title: Great work but leave something to be describe and refine

final_score: rating: 6: Marginally above acceptance threshold
review_content: This paper studied the Semi-supervised problem in multi-organ segmentation. The author used Cross supervision method to achieve good results in the FLARE2022 challenge.

In the method section, the author's description is explained in a clear and readable way. Furthermore, this paper is well structured and the experiments are sufficient.

There are some problems, which must be solved before it is considered for publication. If the following problems are well-addressed:

* It is noted that your manuscript needs careful editing by someone with expertise in technical English editing paying particular attention to English grammar, spelling, and sentence structure so that the goals and results of the study are clear to the reader.

* The caption section of the figure needs to be supplemented with a more detailed description.

* Please recheck the paper with the recommendations in the official supplemental documentation.

review_title: There are some language and formatting issues waiting to be fixed

final_score: rating: 7: Good paper, accept
review_content: In this paper, the authors proposed a siamese network with cross supervision to train the semi-supervised segmentation network. They introduced two same architectures, which were initialized differently. Both networks can generate pseudo label images and supervise each other’s training through cross supervision. 

This paper is technically well written, further supported by preprocessing, postprocessing, and ablation study, and can be accepted for publication; however, there are some grammatical mistakes and typos, such as

(I)  Nonuniform should be non-uniform

(II) Two networks with the same architecture are introduced, and the are initialized differently at the beginning of training (rewrite this sentence)

(III) subnetworks should be sub-networks

(IV) We train the two networks in a cross-supervised way (we should write like “We train the two networks in a cross-supervised manner”)

(V) Please check the whole manuscript for such types of problems and modify it accordingly.

(VI) Fig-2 entails three sub-images; therefore, it is suggested to assign valid numbers to each sub-image, such as (a), (b), (c) or (i), (ii), and (iii) with a clear caption. Likewise, Fig-3 and 4 need some proper explanation.

review_title: In this paper, the authors proposed a siamese network with cross supervision to train the semi-supervised segmentation network. This paper is technically well written, further supported by preprocessing, postprocessing, and ablation study, and can be accepted for publication; however, there are some grammatical mistakes and typos.

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