Cell-APP: A generalizable method for cell annotation and cell-segmentation model training.

ABSTRACT: Deep learning-based segmentation models can accelerate the analysis of high-throughput microscopy data by automatically identifying and classifying cells in images. However, the datasets needed to train these models are typically assembled via laborious hand-annotation. This limits their scale and diversity, which in turn limits model performance. We present 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 nuclear fluorescence images-and operates in two main steps. First, it extracts each cell's location from the nuclear fluorescence channel and provides these locations to promptable deep learning models to generate cell masks. Then,

SUBMITTER: Virdi AJ 

PROVIDER: S-EPMC11785174 | biostudies-literature | 2025 Aug

REPOSITORIES: biostudies-literature