Venue: MIDL 2023 Short_Paper_Track

Reviews:

final_score: rating: 7: Good paper, accept
review_content: The authors show how segmentation accuracy with a standard 3D U-net can greatly improve when the input images are quantized to a small number of bits. This improvement is *massive* (20 Dice points) in one of the tasks. The authors' hypothesis is that lower entropy images facilitate learning in low-data regimes. However, It is difficult not to wonder whether there is a problem with the data normalization or some other component of the setup. Either way, I think that this paper could spark interesting discussion at the conference.

PS: a (very minor) slap on the wrist to the authors, for withholding their names & institutions on the pdf, which saves space (maybe they were not aware that short papers were single-blind - in which case, the slap on the wrist is for not reading the instructions!)
review_title: Unexpected but weird but intriguing results

final_score: rating: 4: Ok but not good enough - rejection
review_content: This paper presents the experimental results of using low-entropy images (reduce bits) as sparse input for deep learning-driven tissue segmentation
The advantages of the paper include: 
+ This paper applies the different configurations of bits on BRATs dataset.
+ Box plots are present to show the variations
The limitation of the paper includes: 
- Many papers have already discussed the impacts of bits on medical image segmentation
- The situation is case by case. Brain tumor is a relatively simple task. The conclusion might not hold for detailed brain tissue segmentation on MRI
- The technical innovation is limited as a 3D U-Net is applied to a different dataset.
- Qualitative results might be helpful for presenting the results.
review_title: Test MRI segmentation performance with image entropy

Decision: Accept(Poster)