Discover Community Create You

Loading publication...

Noodle always counts identifier-free feature events for service operation. With your permission, Google Analytics can also estimate audience and engagement. Search text, publication identifiers, cookies, IP addresses and browser fingerprints are never included in Noodle's first-party event counters.

Privacy details

Article

CellPhePy: A python implementation of the CellPhe toolkit for automated cell phenotyping from microscopy time-lapse videos.

Journal of microscopy - 1 Nov 2025

Wiggins Laura, Lacy Stuart, Park Graeme, Marrison Joanne, Powell Ben, Cimini Beth, O'Toole Peter, Wilson Julie, Brackenbury William J

Abstract excerpt

We previously developed the CellPhe toolkit, an open-source R package for automated cell phenotyping from ptychography time-lapse videos. To align with the growing adoption of python-based image analysis tools and to enhance interoperability with widely used software for cell segmentation and tracking, we developed a python implementation of CellPhe, named CellPhePy. CellPhePy preserves all of the core...

Read the complete abstract on PubMed

Topics

Share this publication in a Topic to start or enrich a Post.

Share to Image Processing, Computer-Assisted
Share to Microscopy

Methodology Data sources Editorial policy Privacy About Contact Helena Bioinformatics

Share to Microscopy, Video

Share to Phenotype

Share to Single-Cell Analysis

Share to Software

Share to Time-Lapse Imaging

Source

Open in PubMed Open full text

Identifiers

PMID: 40275641
DOI: 10.1111/jmi.13416
PMCID: PMC12523983

Provenance

PubMed metadata and Literature Corpus relationships. Corpus snapshot updated 3 Sept 2026.

Related research

Semantic proximity does not establish scientific evidence.

Research trail→

Click a neighbor to travelStep 1 · 12 closest

CellPhePy: A python implementation of the CellPhe toolkit for automated cell phenotyping from microscopy time-lapse videos.PMID 40275641Read ↗

CellPhe: a toolkit for cell phenotyping using time-lapse imaging and pattern recognitionSemantic proximity Read ↗

Segment2P: Parameter-free automated segmentation of cellular fluorescent signalsSemantic proximity Read ↗

NanTex enables computational multiplexing and phenotyping of organelles across super-resolution modalitiesSemantic proximity Read ↗

Development of a universal imaging "phenome" using shape, appearance and motion (SAM) features and the SAM Phenotype Observation Tool (SPOT).Semantic proximity Read ↗

Multiplexed high-content imaging uncovers morphological diversity of lymphocyte activation and dysfunctionSemantic proximity Read ↗

Fast and Accurate Cell Tracking: a real-time cell segmentation and tracking algorithm to instantly export quantifiable cellular characteristics from large scale image dataSemantic proximity Read ↗

EBImage--an R package for image processing with applications to cellular phenotypes.Semantic proximity Read ↗

CellClassifier: supervised learning of cellular phenotypes.Semantic proximity Read ↗

The Allen Cell and Structure Segmenter: a new open source toolkit for segmenting 3D intracellular structures in fluorescence microscopy imagesSemantic proximity Read ↗

Cellquant: a vibecoder’s guide to image analysisSemantic proximity Read ↗

Cellpose as a reliable method for single-cell segmentation of autofluorescence microscopy imagesSemantic proximity Read ↗

Cell Spotter (CSPOT): A machine-learning approach to automated cell spotting and quantification of highly multiplexed tissue imagesSemantic proximity Read ↗

⌁Select a neighboring publication to make it the new centre.