Venue: MIDL 2023 Short_Paper_Track

Reviews:

final_score: rating: 5: Marginally below acceptance threshold
review_content: 
The short paper train a nnU-Net for segmentation of lipid
vacuoles in whole-slide histology images using semi-automatically generated imperfect reference annotations. They evaluated the performance on two out-of-distribution datasets. The trained model had F1 scores of 0.801 and 0.804 using the nnU-Net framework for the automatic segmentation of lipid vacuoles. The results are promising but the methods are quite standard.

review_title: segmentation of lipid vacuoles in whole-slide histology images using semi-automatically generated imperfect reference annotations

final_score: rating: 6: Marginally above acceptance threshold
review_content: This paper trained a nnU-Net to automatically segment lipid vacuoles in whole-slide images. 
The advantages of the paper include: 
It provides source code, qualitative results, and quantitative results in the short paper. 
This is an interesting clinical application.
The limitation of the paper includes:
No baseline methods are included.
The methodological innovation is limited as the paper applies an existing segmentation model.
review_title: nnU-Net for pathological image segmentation

Decision: Accept(Poster)