Venue: NeurIPS 2022 Challenge CellSeg

Reviews:

final_score: rating: 7: Good paper, accept
review_content: This paper proposed a multi-task Mean Teacher for semi-supervised cell segmentation. Experiments also evidenced the effectiveness of the proposed method.

Pros:
- The paper is well-written and easy to follow.
- The authors did a detailed study of how each part of the proposed method will affect the performance.
- Unlabeled images are relatively easy to obtain in practice, utilizing the unlabeled images to achieve better performance is great.

Cons:
- In the ablation study, it seems that SSL only contributes a little to the final score, thus, I hope the authors can give a more detailed study, for example, to show how SSL improves predictions.
review_title: Official Review

final_score: rating: 7: Good paper, accept
review_content: This paper proposed a multi-task Mean Teacher for semi-supervised cell segmentation. Extensive experiments show the superioriy of the proposed approach. 

Pros:
1.  The overall architecture is simple yet effective. The notations and descriptions are easy to follow. 
2.  The authors did additional research on extra cell data, which has proven the effectiveness in improving the segmentation performance.
3.  The proposed model achieved a decent trade-off between the accuracy and efficiency, as shown in Table 5. 

Cons:
1.  I suggest the authors try different weak/strong augmentations beyond cutmix, as these augmentations are the key factors in semi-supervised settings. Also, it would be interesting to see some comparison results for different cell modalities as discussed by the authors 
2. In Table 3, I think the order of these combinations should be changed as 'cutmix', 'color jitter', 'semi-supervised methods' are part of your main model, while elements such as 'post-process', 'multi-scale inference' do not belong to the model design. Better to re-organise them. 

review_title: Official Review

final_score: rating: 8: Top 50% of accepted papers, clear accept
review_content: # Summary  
This paper designed a semi-supervised framework named MT^2 for instance-level cell segmentation from images. The background and method details were demonstrated properly and the comparison and ablation experiments were presented clearly. I recommand the acceptance of this paper.  

# Pros  
1. This paper proposed a mean teacher method consisting teacher-student networks, which is suitable for the semi-supervised cell segmentation task with unlabeled data.  
2. The loss function was designed properly with consideration of pixel classification, cell identification and boundary structures. The task of cell segmentation from images suffer from the overlapping of multiple cells and this paper explores a way to solve this issue.   

# Cons  
1. The post-processing step is important for segmentation task, especially for competitions. This paper also involves this step using the watershed method. Besides providing the thresholds of watershed method, the paper should introduce the principle or intuition of why to choose these thresholds. Also, it would be better to provide some visualization or quantitative evaluations.  
2. As the paper stated in the limitation part, the false positive (FP) is an important issue in segmentation task. I suggest the authors provide the FP values alongside the F1-score for better illustration.  

review_title: Paper Review

Decision: Accept