Venue: MICCAI 2022 Challenge FLARE

Reviews:

final_score: rating: 6: Marginally above acceptance threshold
review_content: A 2D segmentation pipeline was proposed in the article, with a reference module and a propagation module. A method of how to choose pseudo labels was tested.     

Overall, using 2D segmentation model plus video processing technique is not very suitable for medical image processing.  
Many models have been tested on medical image segmentation, While 3D segmentation models had much better performance, comparing with general 2D segmentation model.  Low baseline value in Table 5 with DeeplabV3+ model, was also proved this point.   

Advise to start with some models, commonly used in medical image analysis.   
review_title: Try on well-known 2D segmentation method and video processing method on a medical 3D image segmentation 

final_score: rating: 6: Marginally above acceptance threshold
review_content: Pros:
- the results show that the proposed semi-supervision really matters
- authors put an effort to implement such a complex method

Cons:
- results are not SOTA
- no comparison with 3D-conv models
review_title: Complex 2D model, results are not bad, but are not SOTA

final_score: rating: 6: Marginally above acceptance threshold
review_content: In this paper, the authors adopted mask propagation refinement and uncertainty estimation for CT volume labeling to perform semi-supervised abdominal organ segmentation.

The manuscript is basically complete. Suggestions or deficiencies:

1. In Table 2 and Table 3, the authors should describe the patch size, which seems to be 512×512.
2. In Table 2 and Table 3, the authors should give a more detailed description of Random initialization.
3. In Figure 5, the authors should point out which image each row comes from.
4. Tables 2 and Tables 5 are too wide.
5. The authors should divide section 4 into different subsections.
6. The authors should add segmentation efficiency results and segmentation efficiency analysis in section 4.
review_title: Basically complete but with some deficiencies

final_score: rating: 8: Top 50% of accepted papers, clear accept
review_content: **Summary:**

This work applies Windowing CT for data augmentation, employs DeeplabV3+ and TransU-Net for 2D segmentation task, and adopts CPS for semi-supervised training. A two-stage pipeline is proposed, including the reference module and propagation module, which is clearly explained in section 2. This work is a well-written paper, and its experiments are sufficient and substantial.

**Suggest improvements:**

As discussed in the 'Reference module' section:

> Embedding the slices’s relative position on the axial dimension as a feature vectors and input them to both networks TransUnet and DeeplabV3+ to learn.

How are slice position embedding features fused with feature maps, and how to train the embedding features? It is not explained clearly, and there is no ablation experiment to prove its effectiveness.
review_title: Well-written paper with sufficient ablation experiments

Meta-review: metareview: Please adjust the window width and level of CT images to 40 and 400, respectively in all figures. 
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