Cellpose as a reliable method for single-cell segmentation of autofluorescence microscopy images.

ABSTRACT: Autofluorescence microscopy uses intrinsic sources of molecular contrast to provide cellular-level information without extrinsic labels. However, traditional cell segmentation tools are often optimized for high signal-to-noise ratio (SNR) images, such as fluorescently labeled cells, and unsurprisingly perform poorly on low SNR autofluorescence images. Therefore, new cell segmentation tools are needed for autofluorescence microscopy. Cellpose is a deep learning network that is generalizable across diverse cell microscopy images and automatically segments single cells to improve throughput and reduce inter-human biases. This study aims to validate Cellpose for autofluorescence imaging, specifically using multiphoton intensity images of NAD(P)H. Manually segmented nuclear masks of NAD(P)H ima

SUBMITTER: Riendeau JM 

PROVIDER: S-EPMC11828867 | biostudies-literature | 2025 Feb

REPOSITORIES: biostudies-literature