Venue: MICCAI 2021 Challenge KiTS

Reviews:

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

- You should include references to support your statements and refer the reader to the background on the methods that you used

### Introduction

- Please just just one new-line between these two paragraphs

### Methods

- Please expand on this histogram equalization approach and provide a reference if necessary
- What does "HOG" stand for in the figure? Please define this acronym in the caption
- How did you choose your range that you clipped the images to?
- Section 2.3.2 needs a title

### Results

- The caption for table 1 is missing a space between "in" and "validation"
- Please make sure to include the final challenge results once they are known

### Discussion and Conclusion

- This section should be numbered 4, not 3
review_title: Official Review

final_score: rating: 6: Marginally above acceptance threshold
review_content: The authors present a coarse-to-fine approach using different networks for each segmentation objective (i.e. each of the three classes). The paper is somewhat short on details and could definitely benefit from a figure which compares a predicted segmentation to the ground truth. Also, the authors don't mention which set of aggregated labels they trained on or what custom aggregation method they used, if any. Most used majority voting, is that what you used? Please make sure to include this in the paper.
review_title: Official Review

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