ArticleBio-protocol2026
How to Train Custom Cell Segmentation Models Using Cell-APP.
Article in Bio-protocol, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
The deep learning revolution has accelerated discovery in cell biology by allowing researchers to outsource their microscopy analyses to a new class of tools called cell segmentation models. The performance of these models, however, is often constrained by the limited availability of annotated data for them to train on. This limitation is a consequence of the time cost associated with annotating training data by hand. To address this bottleneck, we developed Cell-APP (cellular annotation and perception pipeline), a tool that automates the annotation of high-quality training data for transmitted-light (TL) cell segmentation. Cell-APP uses two inputs-paired TL and fluorescence images-and operates in two main steps. First, it extracts each cell's location from the fluorescence images. Then, it provides these locations to the promptable deep learning model
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