Venue: MICCAI 2021 Challenge KiTS

Reviews:

final_score: rating: 7: Good paper, accept
review_content: ### Overall

- Please make sure to update the abstract with your performance on the test set once this is known

### Introduction

- Looks good

### Methods

- It might be nice to mention the resampling earlier, perhaps before you mention cropping
- Am I understanding correctly that you simply discard all pixels outside of a 384x384 window? Is this only during training? Please clarify within the paper
- Please explain why you chose your HU bounds for normalization

### Results

- Looks good

### Discussion and Conclusion

- Looks good
review_title: Official Review

final_score: rating: 7: Good paper, accept
review_content: This paper presents a coarse-to-fine approach based on a 3D U-Net. The paper is well-written and contains a good level of detail along with nice figures. One point that was omitted, though, is which set of aggregated segmentations they used for training and validation? Most teams used majority voting, but some used the sampling method from the GitHub repo. The authors should include this detail in their revision.
review_title: Official Review

Meta-review: Please address the reviewer comments and resubmit
Decision: Minor Revisions