Venue: MIDL 2023 Short_Paper_Track

Reviews:

final_score: rating: 7: Good paper, accept
review_content: This paper presents a way of improving VParNet for brain ventricle parcellation without retraining by incorporating CALAMITI  and defining optimal operating contrast (OOC) for image harmonization. 

It is a straightforward integration of data harmonization and deep learning methods, which shows outperformance over the original VParNet.
review_title: Review

final_score: rating: 7: Good paper, accept
review_content: In this paper, the authors assess how tinkering with the contrast of an MRI scan using an unsupervised harmonization technique affects the performance of a segmentation neural network with fixed weights. Very intriguingly, the paper also shows that this contrast is *not* the contrast of the source domain. The paper does not go much deeper into why this may be the case (the space constraints are severe, to be fair)  but I think that it could lead to interesting discussion during the conference. 

PS: a (very minor) slap on the wrist to the authors, for withholding their names & institutions on the pdf, which saves space (maybe they were not aware that short papers were single-blind, in which case, the slap on the wrist is for not reading the instructions!)
review_title: Simple but intriguing result

Decision: Accept(Poster)