Venue: NeurIPS 2022 Challenge CellSeg

Reviews:

final_score: rating: 8: Top 50% of accepted papers, clear accept
review_content: This paper presents a semi-supervised cell instance segmentation framework for multi-modality microscope images. Authors introduce effective methods for different sub-problems, e.g., K-Means for modality discrimination, Cellpose for cell segmentation, U-Net for sub-categories segementation, CDNet with semi-supervised learning for fluorescence segmentation. The overall method achieves very good performance, reaching f1 score of 0.8428.
review_title: Well paper

final_score: rating: 9: Top 15% of accepted papers, strong accept
review_content: Authors divided up the modality of the training images using k-means clustering prior to subjecting each to a specialist model. This is in contrast to the spirit of the competition.
To integrate unlabelled images during training, the authors came up with a cross constraint to regularize predictions using a paired augmented image. This allowed authors to successfully leverage the unlabelled data to improve models on average of ~0.01 across 3/4 of the modalities. The improvements were marginal but the method may still be of interest to the greater audience. 
Implementation details and results were complete and final performance was impressive at 0.8428 on the tuning set. 
Overall very well written.
review_title: Semi-Supervised Cell Instance Segmentation for Multi-Modality Microscope Images

final_score: rating: 6: Marginally above acceptance threshold
review_content: ## Summary
This paper proposed a semi-supervised cell instance segmentation framework for multi-modality microscopy. To address the issue that it is hard for single model to handle diverse modalities, the paper adopted a classification-segmentation strategy. Firstly, it classifies the input images by K-Means algorithm in HSV color space. After that, the corresponding model is selected for the inputs. The experiments verifies the effectiveness of the proposed framework on multi-modality cases.


## Pros:

+ The paper is well structed.

## Cons:

+ The authors may muddle the modalities. It is believed that the DIC Modality is actually Brightfield.

+ The writing should be strengthened. There are some typos in the paper, e.g. color argumentation in Section 2.4.

+ The paper did not provide efficiency results of WSI.

+ There is a general model for phase contrast and brightfield microscopy, therefore the framework may adopt two models during inference.
review_title: Well paper, but still need some modifications

final_score: rating: 7: Good paper, accept
review_content: Summary: 
The authors presented an ensemble framework to solve the cell instance segmentation problem for multi-modality microscropy images. K-Means was applied to select which segmentation network to use. By fine-tuning the Cellpose method, the cell segmentation of phase contrast, DIC, and bright field images is realized.

Pros: 
The paper is well constructed.
Ablation studies confirm the effectiveness of the method with the semi-supervised learning strategy.

Cons: 
The performance on Fluorescence images should be optimized and results should be further discussed.
Model generalization for phase contrast and brightfield images need to be further improved.

review_title: Paper well structed, improvements needed

Decision: Accept