Venue: MICCAI 2022 Challenge FLARE

Reviews:

final_score: rating: 6: Marginally above acceptance threshold
review_content: Pros:1. This method uses a simple but effective self-training method to improve the semi-supervised performance.
Cons: 1. In the Method section, it’s said that the preprocessing cut the case into slices, however the proposed method illustrate a 3D segmentation framework. Does the slices indicate the sub-part like patches?
2. This training only use 200 unlabeled images, and there is no explanation for the usage of only 200 unlabeled images and how these images were selected.

review_title: This article simply explained their method while the details and more information should be included.

final_score: rating: 7: Good paper, accept
review_content: The paper is clear enough. Authors prove the efficiency of self-training in FLARE2022.
review_title: Review of Abdominal Organ Segmentation via Self Training

final_score: rating: 5: Marginally below acceptance threshold
review_content: Weakness:
1. Fig.1 needs to be redrawn, please do not use the example figure in the template.
2. Describe your self-Training method and draw the flow picture in the method section.


Advice:
1. In Fig.2, better adjust CT Hu to [-325,-325].
review_title: MICCAI-FLARE

final_score: rating: 4: Ok but not good enough - rejection
review_content: In this paper, a semi-supervised method using nnUNet to generate pseudo labels is proposed to continuously improve the segmentation effect of the model. However, this paper does not mention the final prediction part of the network and the prediction efficiency of the network. If the original framework of nnUNet is used as the prediction, it will be very time-consuming. In addition, this paper is too short.
review_title: This paper lacks a description of the prediction efficiency. In addition, this paper is too short.

final_score: rating: 5: Marginally below acceptance threshold
review_content: Strengths: The proposed method achieves effective semi-supervised learning via self-training with a mean DSC of 0.8568.

Weaknesses: The two main aspects of the FLARE22 challenge are efficiency and how to use unlabeled data. Although the proposed method was a very effective semi-supervised learning strategy, the paper does not present any methods or results regarding memory usage or inference time, which would be a bit out of line with the purpose of this challenge. Adding the segmentation efficiency analysis results would be sufficient for acceptance.
review_title: Abdominal Organ Segmentation via Self Training

final_score: rating: 8: Top 50% of accepted papers, clear accept
review_content: * In Table 2, the hyper-parameters of lr decay schedule should be declaim.
* In Table 3, the table column names should be tapped.
* In Fig 2, the ids of image is not very clear.

Over all, good job but still need to refine.
review_title: Layout issue

final_score: rating: 7: Good paper, accept
review_content: 1. The descriptions of the method and the network are clear.
2. The authors need to highlight their contributions and results.
review_title: clear description of the method

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