Toggle-Untoggle - a cell segmentation tool with an interactive user verification interface

Accurate cell segmentation is an essential step in the quantitative analysis of fluorescence microscopy images. Pre-trained deep learning models for automatic cell segmentation such as those offered by Cellpose perform well across a variety of biological datasets but might still introduce segmentation errors. Although training custom models can improve accuracy, it often requires programming expertise and significant time, limiting the accessibility of automatic cell segmentation for many wet lab researchers. To address this gap, we developed 'Toggle-Untoggle', a standalone desktop application that enables intuitive, code-free quality control of automated cell segmentation. Our tool integrates the latest Cellpose 'cyto3' model, known for its robust performance across diverse cell types, while also supporting the 'nuclei' model and user-specified custom models to provide flexibility for a range of segmentation tasks. Through a user-friendly graphical interface, users can interactively toggle individual segmented cells on or off, merge or draw cell masks, and export morphological features and cell outlines for downstream analysis. Here, we demonstrate the utility of Toggle-Untoggle in enabling accurate, efficient single-cell analysis on real-world fluorescence microscopy data, with no coding skills required.