Venue: MICCAI 2021 Challenge KiTS

Reviews:

final_score: rating: 5: Marginally below acceptance threshold
review_content: ### Overall

- Once results are known, it would be nice to add a statement about how well it worked to the abstract

### Introduction

- There are some instances where spaces are omitted after sentences end, please fix those
- When referring to other works, please use the convention of "Lastname et al." rather than first name or last, first as you have done

### Methods

- Which method did you use to resample the imaging data? What about the segmentations?
- How did you chose your weighting in the loss function?

### Results

- It would be nice to show a figure with an example of your predictions here
- Please be sure to add the official results once they are known

### Discussion and Conclusion

- Please define the acronym "HU" before using it
review_title: Official Review

final_score: rating: 6: Marginally above acceptance threshold
review_content: The authors present an approach that makes use of the nnU-Net after resampling to 2mm and sampling patches in such a way that oversamples "occupied" rather than background areas. It is not stated whether the authors made use of the multiple annotations per case or simply used the majority-voting approach. Beyond that, the paper has an adequate level of detail, but another figure would be nice, either to show predictions vs ground truth or a learning curve, etc.
review_title: Official Review

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