Venue: MICCAI 2022 Challenge FLARE

Reviews:

final_score: rating: null
review_content: In this work, the authors proposed a PCLR and V-Net-based method to segment abdominal organs for FLARE 2022 challenge.

Strengths: A novel method based on PCLR and V-Net

Weaknesses: An Engineering method with low mean DSC and NSD.

Suggestions for authors:

1: If your preprocessing method is based on some previous work/works, please indicate that.

2: Throughout your paper, there are some English grammar mistakes and typos, such as
Selfsupervised

adopted as a practical[2], on the original V-Net[7], and PCRL[11], abdominal

3: You don’t have any postprocessing method; how you make assure that the final predictions are fine rather than coarse? Or what alternative do you have to achieve fine segmentation outcomes?

4: No clear methodology; please revisit your method and modify it accordingly.

5: Add an ablation study.


review_title: In this work, the authors proposed a PCLR and V-Net-based method to segment abdominal organs for FLARE 2022 challenge with low mean DSC and NSD

final_score: rating: 6: Marginally above acceptance threshold
review_content: Inspired by retained contrastive learning, this paper proposes a V-Net, which uses unlabeled data for self pre training, and uses labeled data for fine tuning. The article has certain innovation points and high integrity, but the proposed methods did not achieve better expected results, and there is a lack of relevant discussion in some chapters, which will appear in the list of suggestions below.

Here are some suggestions for improvement:
1. Figure 3 should be adjusted, and it should not occupy a page of space.
2. The article should properly discuss the possible reasons for the decline of other organ indicators after using the self training model.
3. The article should describe the reasons for not using post-processing
4. For the loss function, the article should properly describe why to use the comparative loss and MSE loss in the self training stage.
review_title: A method based on V-Net which uses PCRL for abdominal organ segmentation

final_score: rating: 4: Ok but not good enough - rejection
review_content: The authors designed a preservational contrastive representation of learning (PCRL) using different argumentations on unlabeled data to get the self-pre-trained network weight.

In Sections Experiments, PCRL has only improved segmentation performance on the right adrenal gland (RAG) and left adrenal gland (LAG).

There are some problems, which must be solved before it is considered for publication. If the following problems are well-addressed:

* It is noted that your manuscript needs careful editing by someone with expertise in technical English editing paying particular attention to English grammar, spelling, and sentence structure so that the goals and results of the study are clear to the reader.

* compressing the input size and utilizing a smaller width is not available as a contribution
* The contrastive loss mentioned in your method is not clearly defined
* In Section 2.2, The detailed part of the PCRL framework is not elaborated on. For example, whether different data augmentation is used for different encoders and whether the outputs of different encoders are used directly without any regulation.
* The overall results of your method are worse and the damage to performance is not analyzed at the method level.

review_title: More weaknesses in the paper itself

final_score: rating: 5: Marginally below acceptance threshold
review_content: Comments to the Author  

In this paper, building on previous work, the authors apply self-pretrained V-net based on PCRL for abdominal organ segmentation. But I found that for the task of this paper, the authors' results have room for further improvement.

- There is extra unreadable information in the author column
- In the fourth line, "provide" should be replaced with "provided".
- In their contribution, the authors compress the input size and use a smaller network width in order to reduce resource consumption and speed up the reasoning process. Does this affect the original accuracy of the model? And how?
- What is the meaning of the lack of declaration of some of the ICONS in Fig. 1, and the "Argumentation"? Augmentation is it? Hope to give the explanation for the convenience of readers.
- Delete section 2.3.
- In Table 3, the authors use bold font for control, but it is not shown on organs such as "Gal."
- The text of this article needs further polishing.
- etc.. 

Please go through the paper and improve the experimental results and wording.
review_title: the authors' results have room for further improvement.

final_score: rating: 6: Marginally above acceptance threshold
review_content: # Summary

The method first performs self-supervised learning on the unlabeled data using Perservational Contrastive Representation Learning (PCRL) and finetuning the pre-trained model by performing supervised learning on the labeled data. Half precision training was used to reduce the load to the GPU with a small tradeoff to the performance.

# Strengths

- Uses self-supervised learning (PCRL) to harness the unlabeled data
- Focused to reduce the resource comparison making the maximum GPU memory used 1719 MB

# Weaknesses

- The methodology mainly covers the PCRL portion foreshadowing the actual work
- No proper analysis of results on the validation set

# Details
- Floar64, Float32, and Float16 are known as double-precision, full-precision, and half-precision. Either the author has mistakenly written Float32 for half-precision or no work was done on reducing the precision.
- In the third paragraph of **Introduction**, the citation for _residual U-net_ is wrong; it is the same as that of V-Net
- The idea of horizontal plane scaling and vertical sliding windows is not clear. Which axis do you mean by horizontal and vertical?
- The resampling method for anisotropic data is not clear about the transverse plane and axis spacing.
- The final layer for the prediction should be softmax not _sigmoid_ since it is a multi-class classification, not multi-label
- _an area with a length of 80 on the axis is randomly cropped_ no idea on what's going on here.
- The loss is Dice Loss in Table 2 but Generalized Dice in other portions of the paper
- The rows seem the same for Tables 3 and 4. A notation should be kept in the table itself rather than in the description for readers' clarity.
- The author has talked about 20 validation cases but which cases are not clear. The provided cases are 50 in number; do you mean the internal validation set?
- How can the sliding window strategy consume more time and waste unnecessary resources in comparison to whole volume input?
- No analysis for Figure 3 by the author himself and the figures are too small to be analyzed by the readers.
review_title: Same as that of Paper 16: PCRL-inspired Self-supervised Training

Meta-review: metareview: Reviewers raise many concerns and suggestions. Please address all comments in the revised manuscript.
confidence: 5: The area chair is absolutely certain
Decision: Major Revision