Author's Reply to MoNuSAC2020: A Multi-Organ Nuclei Segmentation and Classification Challenge

We had released MoNuSAC2020 as one of the largest publicly available, manually annotated, curated, multi-class, and multi-instance medical image segmentation datasets. Based on this dataset, we had organized a challenge at the International Symposium on Biomedical Imaging (ISBI) 2020. Along with the challenge participants, we had published an article summarizing the results and findings of the challenge (Verma et al., 2021). Foucart et al. (2022) in their Analysis of the MoNuSAC 2020 challenge evaluation and results: metric implementation errors have pointed ways in which the computation of the segmentation performance metric for the challenge can be corrected or improved. After a careful examination of their analysis, we have found a small bug in our code and an erroneous column-header swap in one of our result tables. Here, we present our response to their analysis, and issue an errata. After fixing the bug the challenge rankings remain largely unaffected. On the other hand, two of Foucart et al.'s other suggestions are good for future consideration, but it is not clear that those should be immediately implemented. We thank Foucart et al. for their detailed analysis to help us fix the two errors.

Automated summary

This passage mentions the release of MoNuSAC2020 as a large-scale medical image segmentation dataset and the organization of a corresponding challenge. The authors found minor errors in their code and result tables and responded to them with an errata. Additionally, based on the analysis by Foucart et al., the authors mention some suggestions that are worth considering but may not be immediately implemented.