Toggle-Untoggle: A cell segmentation tool with an interactive user verification interface.

Abstract

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 Cellpose, offer strong performance across a variety of biological datasets but may still introduce segmentation errors. While 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 desktop application that combines automated segmentation using the Cellpose "cyto3" model with a user-friendly graphical interface for intuitive segmentation quality control. Our app allows users to refine results by interactively toggling individual segmented cells on or off without the need to manually edit segmentation masks, and to export morphological data 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.