Cell Image Analysis Techniques

Cell Painting PLUS reveals phenotypic signatures for nephrotoxicity profiling in human RPTEC/TERT1

This study uses Cell Painting PLUS to identify nephrotoxicity-related morphological signatures in human RPTEC/TERT1 cells by analyzing over 300 compounds.

HERO: A hierarchy-aware analysis pipeline for reducing and refining whole-brain atlas-mapped cellular datasets

HERO reduces and refines whole-brain atlas-mapped cellular datasets by performing hierarchy-aware selection, refinement, and visualization, improving interpretability and reproducibility.

Automated compartment-based quantification reveals distinct patterns of cutaneous innervation with aging and microbial colonization

The paper develops an automated image analysis pipeline to quantify cutaneous innervation in different skin compartments, revealing age and microbial effects on nerve area and length.

GENIE: a multi-agent system for closed-loop autonomous microscopy

GENIE creates an autonomous microscopy system that translates natural language goals into executable experiments and statistical analyses.

When label-free morphology is sufficient for targeted cellular measurements

The paper evaluates whether label-free morphology can replace targeted cellular measurements by analyzing paired phase-reporter data across gene knockouts and screens.

SatelliteQ: an open-source workflow for multidimensional, object-based quantification of centriolar satellites

SatelliteQ quantifies centriolar satellites in 2D, 3D, and time-lapse images, revealing spatial and numerical changes linked to cellular states and disease.

A human-in-the-loop approach for faster cell tracking

This paper introduces a human-in-the-loop cell tracking framework that integrates human feedback during tracking to reduce correction needs and improve track quality.

Archetype analysis of multiplexed antibody imaging reveals interpretable morphological programs in optical pooled screens

The paper introduces CellMAPP and CHART to analyze multiplexed antibody imaging data, revealing modular morphological programs in response to genetic perturbations.

Quantification of cellular contributions to multi-cellular pattern formation

This paper introduces a geometric method to quantify individual cellular processes' contributions to multi-cellular pattern formation using synthetic and experimental data.

VesiScope: A stand-alone tool for the automatic detection, quantification and size analysis of GUVs from phase contrast microscopy images

VesiScope detects and analyzes GUVs from phase contrast images using a YOLOv11 model, enabling accurate and efficient label-free quantification.

Machine learning classifies subcellular Golgi morphology from imaging flow cytometry during inflammatory activation: an exploratory study

This study uses deep learning to classify Golgi morphology from imaging flow cytometry data to distinguish LPS-treated from control cells in human and mouse immune cells.

FilAdh: degradation-aware analysis of focal adhesions and actin stress fibers

FilAdh is a framework for analyzing focal adhesions and actin stress fibers, achieving high accuracy in segmentation, reconstruction, and classification while revealing measurement biases under degradations.

Mastodon: the Command Center for Large-Scale Lineage-Tracing Microscopy Datasets

Mastodon is a software platform for analyzing large-scale lineage-tracing microscopy datasets, enabling manual and automated cell tracking and lineage analysis in complex 3D geometries.

The female larval fly CNS connectome: first draft

The paper releases a complete larval Drosophila CNS connectome with automated segmentations and highlights the need for improved machine vision for proofreading.

CelFDrive: Artificial Intelligence assisted microscopy for automated detection of rare events

CelFDrive automates high-resolution 3D imaging of cells using deep learning for efficient detection of rare events in large populations.

Deep Learning-Meets-Active Surfaces: A Unified Framework for Multi-Object Segmentation and Shape Analysis in 3D Fluorescence Microscopy

This paper introduces Nagini3D, a 3D image segmentation method combining deep learning and active surfaces for improved shape analysis and consistency across 3D volumes.

NucLogic: Logic-based geometric 3D nuclear segmentation for fast and robust analysis of challenging biological imaging data

NucLogic provides a fast and robust 3D nuclear segmentation method that outperforms existing tools at low SNR and enables quantitative analysis of challenging biological imaging data.

AI-assisted quantification of the clonogenic assay

This paper develops an AI framework for automated clonogenic assay analysis, showing it matches expert performance and enables continuous phenotypic profiling of radioresistant cells.

SIGMA: SIgnaling via Golgi Morphology Analysis for pathway-state classification

SIGMA uses Golgi morphology to classify signaling pathways with high accuracy across multiple conditions and compounds.

Build heatmaps of cell frequencies and marker intensities for correct interpretations

The paper describes how to construct heatmaps for cell frequency and marker intensity using Excel, Phantasus, or R to improve interpretation of high-content cytometry data.

SkeleSketch: An Open-Source Tool for Quantifying Microglial Morphology in Fiji/ImageJ

SkeleSketch is an open-source Fiji/ImageJ plugin that automates microglial skeletonization and morphological quantification, showing accuracy across labeling strategies and correlation with commercial software.

A Novel Open-Source CellProfiler Pipeline for Automated, User-Friendly Hierarchical and K-Means Clustering of Microglial Morphology

The paper introduces a CellProfiler pipeline for automated clustering of microglial morphology using hierarchical and k-means methods, enabling user-friendly analysis of morphological subgroups.

Biologically grounded cell profiling across microscopy modalities

The paper introduces MorphAgent, a biologically grounded AI framework for cell profiling across microscopy modalities, improving feature design and performance in drug screening and disease analysis.

Luxar: Gaussian splatting for microscopy and scalable interactive web visualisation of multidimensional scientific data

Luxar compresses volumetric microscopy data into compact archives for interactive web visualization without requiring specialized software.

Autoencoders for unsupervised analysis of rat myeloarchitecture

This study uses unsupervised deep learning to analyze rat brain histology, revealing tissue organization and detecting pathological changes without manual annotations.

3D-MAESTRO: A scalable, modular, portable pipeline for automated processing of large-scale volumetric brain microscopy data

3D-MAESTRO automates processing of large-scale volumetric brain microscopy data, enabling scalable and reproducible analysis of mouse brain structures.

Deep learning extracts MoA-specific signatures from high-throughput images of chemically and genetically perturbed Corynebacteria

This paper develops a deep learning model to identify drug modes of action from high-throughput images of chemically and genetically perturbed Corynebacteria.

An open imaging and AI resource enabling unbiased quantification of extrachromosomal DNA at scale

The paper provides an open resource for automated ecDNA quantification, comparing methods and introducing ecCount to reduce count bias.

LiFT: Live foci tracking for quantitative analysis of DNA damage dynamics

LiFT automates the quantification of DNA damage foci dynamics in live-cell imaging, enabling reproducible and scalable analysis of DSB kinetics in cancer research.

Self-Supervised Discovery of Discrete Local States in Noisy Image Sequences

This paper introduces a self-supervised framework to discover discrete local states in noisy image sequences without clean targets or labels, achieving high precision in particle tracking and separating biological structures from artifacts.