Venue: MICCAI 2022 Challenge FLARE

Reviews:

final_score: rating: 7: Good paper, accept
review_content: Most training details are descripted, but the adopted semi-supervised learning method is missing.
review_title: Review of Coarse to Fine Automatic Segmentation of Abdominal Multiple Organs Based on Semi-Supervised Network

final_score: rating: 7: Good paper, accept
review_content: Advance:
'a stable three-stage fast automatic segmentation method for abdominal 13 organs ' is a good idea.

Weakness:
Lack of DSC and NSC analysis at each stage.

Advise:
In terms of time, it can be accelerated using TensorRT.
review_title: MICCAI-FLARE

final_score: rating: 7: Good paper, accept
review_content: # Summary

The paper presents a segmentation method with three stages: global locating, organ locating, and organ segmentation. The method first locates coarsely where the abdomen is and then locates the individual organs from the abdomen. Then the high-resolution segmentation is generated from those organ areas in the third stage. 

# Strengths
- Efficient and rich organ segmentation method with a relatively low trade-off in metrics.
- Details of the thought process are presented in each step viz. preprocessing, segmenting, and post-processing
- Very low GPU memory consumption (1953 MB) during inference
- Results for each stage show the importance of each stage to reach the current milestone
- Good analysis for qualitative results on the validation set

# Weakness
- Many steps make the method almost hand-engineered
- No explanation on the method used for semi-supervised (pseudo-label or consistency regularization)
- Made in MS Word without aligning with the provided style guide. The paper seems completely different than others.

# Details
- It would be better to mention the name of the stages in the abstract instead of just mentioning their numbers
- Why is only the first-fold validation set result mentioned in the abstract?
- What does making _maximum in-plain resolution 128x176_ mean for a 3D CT scan?
- The description of the model in section 2.2 is not clear, especially _Each layer of the encoding path contains two 3 × 3 × 3 convolution layers, each followed by a ReLu layer, followed by a 2 × 2 × Maximum pool layer with step size of 2 in each direction of 2. In the decoding path, each layer contains a 2 with a step size of 2 × 2 × 2, followed by two 3 × 3 × 3, each followed by a RuLu layer_.
- The first stage can be formulated as a binary segmentation task; whether a pixel is an abdomen or not. Making multi-class output for the stage makes little to no sense.
- Since the training strategy of semi-supervised learning is not mentioned, I suppose it is using the pseudo-labels as a label. But you need to mention the strategy explicitly for the readers.
- No reasoning behind why only 50 unlabeled data were used and how there were selected.
- No reasoning behind why semi-supervised learning was used in the first two stages.
- The input data size seems to be variable from section 2.1. How did you convert the CT scan to _64×128×176_?
review_title: A Three-Stage Fast Automatic Segmentation 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