Venue: NeurIPS 2022 Challenge CellSeg

Reviews:

final_score: rating: 5: Marginally below acceptance threshold
review_content: Summary and Contributions
The author proposed a simple yet efficient processing time by combining YOLO and U-net.

Strengths
- Providing sufficient details of the YoLo methods.
- Interesting empirical ideas to reduce the inference time. 

Weaknesses:
- Lack of detail on the semantic segmentation task. In section 2.3: "the input images are the area that inside the bounding boxes," is that means each box obtained in the detection model is cropped to feed to the semantic model? 
- The writing need to be checked and improved in the final version.  For example, the redundant words "Your idea Therefore" in the introduction part.
- Punctuation is misplaced in some places or missing.
- The quality of the figures needs to be improved as well, both in terms of the resolution and the content. 

review_title: Incomplete paper; therefore, it is hard to follow.

final_score: rating: 4: Ok but not good enough - rejection
review_content: ### Summary and Contributions
The authors propose a two-stage instance segmentation pipeline (detection and segmentation) to extract regions of single cell from five different modalities of microscopy images (phase-contrast, bright field, DIC, fluorescent, and un-annotated). The authors claim the two-stage pipeline reduces inference time and GPU memory compared to standard segmentation methods (i.e. Cascade R-CNN by Cai et al. and Masked-Attention Mask Transformer by Cheng et al.).

### Strengths
- Good description of methods 
- Clear diagram in Figure 1 (though minor typos) that illustrate the main idea of the proposed methods

### Weaknesses
- Figures 3, 4 and 5 seem to be from other papers - please reference them properly.
- Figure 6 is unclear in what it is trying to convey. 
- (Very minor) Proper LaTeX for GIoU equation (use \cup for union, for example) 
- (Very minor) Please write out what NMS and WBF stands for before using the acronym, e.g. Weighted Box Function (WBF). This makes the paper easier to read for others.
- Section 3 on Experiments and Section 4 on Results are very lacking in detail (especially Results).
- Overall, it is hard to verify whether the proposed model improves on existing models and the paper does not do a sufficient job in demonstrating the capabilities with respect to existing methods. 
- There is a clear limitation of the method when cells are crowded - a discussion of this in Limitations and proposal on how to overcome such problems would strengthen the paper. 

### Clarity
- As another reviewer noted, many of the claims of the paper need to be substantiated. As a first draft the paper does well in explaining the proposed methods, but will need significant improvement in explaining its results. 

review_title: YOLOv5 and UNet for Instance Segmentation of Cells

final_score: rating: 6: Marginally above acceptance threshold
review_content: ## Summary
In order to improve the efficiency of multi-instance segmentation, the author proposes a two-stage detection-segmentation model. In the target detection stage, multiple YOLOv5 models were integrated with WBF to fuse the target boxes. The instance segmentation stage uses the UNet which uses EfficientNet as the encoder.

## Methodology
1. Mosaic data augmentation, which spells 4 images into 1. It can enrich the background information of the sample.
2. Adaptively resize the YOLO initial anchor box based on the training set.
3. Object detection uses GIOU LOSS, which is better optimized than IOU LOSS when the predicted box and the true box do not intersect.
4. Only the labeled data provided by the competition is used. Public datasets, pre-trained models, and unlabeled data are not used.

## Comments
1. It would be better to try other models related to UNET in segmentation stage, or make some structural improvements.
2. Part 4.3 gives the inference time of each image segmentation of the verification set, but there is no comparison with other methods, so it is hard to know how fast the inference is.

## Quality
The method proposed in this paper reduces the inference time by 50% compared with the mainstream method while the accuracy is greater than or equal to that of the mainstream instance segmentation method.

## Clarity
1. Steps 2 and 5 of adaptive sizing of the initial anchor box are not explained very clearly. With regard to "Take the remainder to get 8 pixels", who takes the remainder from whom? How does "Using K-means clustering to get n anchor frames" determine n anchor frames, and what is the relationship between k and n?
2. UNET does not give the model structure diagram or the parameter table of each layer.
3. The inferred time is 50% less than the mainstream instance segmentation method ". However, it does not specify the specific method, nor does it give a comparison with other specific methods.

## Conclusion
1. The main contribution of this paper is to improve the inference speed of multi-instance segmentation.
2. The motivation was to solve the problem of slow inference caused by too many examples in a single picture, but the experimental results were not good enough on the picture with multiple small targets.
review_title: Yolo and Unet for Cell Instance Segmentation

final_score: rating: 4: Ok but not good enough - rejection
review_content: The paper ensembles five YOLOv5 to detect cells and uses WBF to remove nested boxes. The obtained patches are fed into the UNet model for cell segmentation. The paper is difficult to read and has a lot of spelling errors and word spacing errors. Too many commas, Incomplete and unclear sentences. Please read your submission carefully before submission.

Method:
*) For the YOLOv5, why did you use five models? The more, the better? Is there a reason for designating five?

*) Even if the neighbor cell is very close to the center cell, does it still work well? Based on what criteria did the center cell and neighbor cell be divided?

*) Figure 7 is unclear. Make sure to accurately express what each black box represents (It looks like the black box was written in a blue box)


Experiments:
*) Please show the difference between the existing method and the proposed method in numbers.


Results and discussion:
*) Please summarize 4.3 (Segmentation efficiency results on validation set) and write it sincerely. If there is a new method you suggested, please fill it out sincerely compared to the previous method.
review_title: Incomplete and unclear sentences.

Meta-review: Although we accept the manuscript, please change fig4 to cell images.
Decision: Accept