ArticleScientific reports2025
Cellpose as a reliable method for single-cell segmentation of autofluorescence microscopy images.
Article in Scientific reports, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers.
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Who cites it
15 citing papers in PubMed.
- Organoid Intelligent Morphomics: Decoding the organoid morphome through artificial intelligence from phenotypic quantification to mechanistic insight.Bioactive materials · 2027Review
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- FLIM Playground: An interactive, end-to-end graphical user interface for analyzing single cells with fluorescence lifetime imaging microscopy.Cell reports methods · 2026Article
- Enteric and Sympathetic Nervous System Pathways Mediate Early Life Stress Effects on Gut Motility and Pain: Mechanistic Findings With Human Correlation.Gastroenterology · 2026Article
- PIGMENT: A deep learning framework for Porcine Immunohistochemistry seGMENTation.bioRxiv : the preprint server for biology · 2026Article
- A deterministic method for quantifying spindle-shaped cells in noisy bright-field microscopy.Scientific reports · 2026Article
- NucleoNet and DropNet: Generalist deep learning models for instance segmentation of nuclei and lipid droplets from electron microscopy images.bioRxiv : the preprint server for biology · 2026Article
- An automated image analysis pipeline for wide-field optical redox imaging of patient-derived cancer organoids.Scientific reports · 2026Article
- Enhanced Precision of Fluorescence In Situ Hybridization (FISH) Analysis Using Neural Network-Based Nuclear Segmentation for Digital Microscopy Samples.Sensors (Basel, Switzerland) · 2026Article
- Article
- AI-driven virtual cell models in preclinical research: technical pathways, validation mechanisms, and clinical translation potential.NPJ digital medicine · 2025Review
- FLIM Playground: An interactive, end-to-end graphical user interface for analyzing single cells with fluorescence lifetime imaging microscopy.bioRxiv : the preprint server for biology · 2025Article
- Segger: Fast and accurate cell segmentation of imaging-based spatial transcriptomics data.bioRxiv : the preprint server for biology · 2025Article
- Consensus guidelines for cellular label-free optical metabolic imaging: ensuring accuracy and reproducibility in metabolic profiling.Journal of biomedical optics · 2025Article
- Deep and dynamic metabolic and structural imaging in living tissues.Science advances · 2024Article
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10 authors.
Funding
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 images were used to train a new autofluorescence-trained model (ATM) in Cellpose for nuclear segmentation of NAD(P)H intensity images. These models were applied to PANC-1 cells treated with metabolic inhibitors and patient-derived cancer organoids (9 patients) treated with chemotherapies. These datasets include co-registered fluorescence lifetime imaging microscopy (FLIM) of NAD(P)H and FAD, so fluorescence decay parameters and the optical redox ratio (ORR) were compared between masks generated by the new ATM and manual segmentation. The Dice score between repeated manually segmented masks was significantly lower than that of repeated ATM masks (p < 0.0001) indicating greater reproducibility between ATM masks. There was also a high correlation (R
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