Peer Review Report For: BISCUIT: An Open-Source Platform for Visual Comparison of Segmentation Models in Bioimage Analysis [version 1; peer review: 2 approved with reservations]

Background Segmentation in microscopy images is a critical task in bioimage analysis, with many deep learning models available (e.g., Cellpose, Omnipose, StarDist, SAM-based models). However, researchers often face challenges in choosing the most suitable model for their data, as quantitative metrics do not always reflect the biological relevance of segmentation results. Methods We developed BISCUIT (BioImage Segmentation Comparison Utility and Interactive Tool), an open-source platform that enables users to run multiple state-of-the-art segmentation algorithms on the same images and visually compare their outputs side-by-side. BISCUIT is implemented as an interactive Jupyter Notebook pipeline, leveraging existing segmentation libraries, and can be executed either via a zero-installation cloud environment (Google Colab) or on local high-performance computing resources. Results Using BISCUIT, we demonstrate how visual inspection of segmentation outputs can reveal qualitative differences between algorithms that may be overlooked by abstract performance metrics. For example, in a fluorescence microscopy image dataset, BISCUIT allowed direct comparison of segmentations from Cellpose, Omnipose, and StarDist, highlighting differences in how each algorithm delineated cell boundaries. This visual approach helped identify the model that produced the most biologically plausible segmentation for the dataset. Conclusions BISCUIT provides an intuitive platform for bioimage analysts and life scientists to evaluate and “see what really works” on their data. The platform is openly available and extensible, lowering the barrier for researchers to perform rapid, interactive benchmarking of segmentation models on their own microscopy data.