fluorescence / live-cell microscopy

ASD Neuro Agentic (ANA): An Agentic Framework for Feature Discovery and Cross Site Generalization in Autism fMRI Classification

The paper introduces ANA, an agentic AI framework that improves cross-site generalization in ASD fMRI classification by reducing data leakage and achieving higher accuracy than existing models.

Microenvironmental Fingerprint of Maturing Tau Condensates from Spectral and Lifetime Phasor Analysis of a Solvatochromic Dye

This paper develops a multimodal framework using spectral and lifetime phasor analysis to characterize microenvironmental changes in Tau condensates during maturation.

Linking Covariates to State Durations in Dynamic Brain Networks via Hidden Semi-Markov Models

This paper introduces a covariate-dependent hidden semi-Markov model to analyze brain network state durations, finding that age and sex influence dwell times in specific brain states.

BEHAV3D-Explorer enables natural language-assisted profiling of 3D cellular dynamics

BEHAV3D-Explorer provides a user-friendly platform for analyzing 3D live-cell imaging data using natural language guidance to select analysis strategies and configure workflows.

Multimodal cellular context enables molecular phenotyping from single-molecule trajectories

The paper introduces MIST, a framework that integrates single-molecule trajectory data with cellular context to improve molecular phenotyping in E. coli.

MitoDecode: quantitative phenotypic decoding of mitochondrial stress states from fluorescence images

MitoDecode assesses mitochondrial stress types and severity from fluorescence images without segmentation, showing adaptability across cell types and stress conditions.

Automahout: an autonomous image analysis system for cell tracking

Automahout autonomously tracks cells in time-lapse images using a self-tuning system that annotates, trains, and refines models without human input, achieving high TRA scores on public datasets.

2D U-NET Deep Learning Architecture for Atherosclerotic Lesion Segmentation and Quantification

This paper develops a 2D U-NET model to segment and quantify atherosclerotic lesions in mouse aortic sections, achieving high accuracy and significantly reducing measurement time.

Evolution of Vesicle Shape Parameters Based on A Deep-Learning Based Image Analysis Framework Over Extended Time Periods

The paper uses a deep-learning framework to analyze shape dynamics of GUVs over time, measuring morphometric changes and budding behavior.

PhenoMe: An interactive AI framework for interpretable, microscopy-driven phenotyping

PhenoMe provides an open-source framework for analyzing microscopy images using foundation model-derived embeddings and experimental metadata to enable phenotyping and data exploration.

Label-free identification of bacterial species using adapted vision transformers

This paper develops a vision transformer model for rapid bacterial species identification from time-lapse images without fluorescent labels, achieving high accuracy on clinical isolates.

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.

Topology-dependent FRET efficiency in living cells via N-C swapping of fluorescent protein fusions

This study shows that the topology of fluorescent protein fusions, not just distance, affects FRET efficiency in living cells, with donor-acceptor configurations showing higher efficiency than acceptor-donor.

Deep learning of fossil pollen morphology reveals 25,000 years of ecological change in East African grasslands

This paper uses deep learning to analyze fossil grass pollen morphology and reconstruct 25,000 years of ecological change in East African grasslands, showing reduced diversity and shifts in C3:C4 ratios during the last ice age.

Self-supervised Internal Learning Enhances Isotropic Resolution for Three-dimensional Fluorescence Microscopy

This paper introduces DeepIso, a self-supervised framework that enhances isotropic resolution in 3D fluorescence microscopy by restoring axial frequency content without requiring explicit PSF models or system calibration.

Segment Any Plant (SAP): Foundation-Model Segmentation for Plant Time-Series Phenotyping

SAP provides a transferable, training-free framework for automated segmentation of plant time-series imagery across species and conditions.

Independent noise realizations enable morphologically agnostic image reconstruction in single-photon-sensitive microscopy

This paper introduces a morphology-agnostic regularization framework for image deconvolution in single-photon-sensitive microscopy, eliminating semi-convergence and improving reconstruction quality.

Multiphoton tomographic fluorescence lifetime imaging microscopy -TomoFLIM

This paper introduces TomoFLIM, a high-speed multiphoton FLIM technique that enables rapid, accurate imaging of dynamic biological processes with compression ratios over 90%.

mAIcrobe: an open-source framework for high-throughput bacterial image analysis

mAIcrobe provides an open-source framework for bacterial image analysis using deep learning models, enabling analysis of diverse bacterial species and microscopy modalities.

PSF-Driven Spatio-Temporal Blending in Fluorescence Lifetime Imaging Microscopy and Its Mitigation via Mean-Shift Super-Resolution-Based Masking.

This paper introduces a method to mitigate spatio-temporal blending in FLIM by using MSSR-based masking, improving spatial resolution while preserving lifetime accuracy.

From video to encounter histories: individual identification and machine vision for salmonid capture-recapture monitoring

This paper develops a machine-vision workflow to convert video footage of salmonids into individual encounter histories for capture-recapture analysis, achieving high accuracy in fish detection and re-identification.

Cellquant: a vibecoder's guide to image analysis

One Python script, configured through LLM-assisted command-line calls, segments and quantifies 2D/3D multi-channel fluorescence images with provenance records; in a yeast temperature series it exposed a non-responding subpopulation.

A Novel Technique for Fluorescence Lifetime Tomography

This paper introduces a deep learning method called AUTO-FLI for 3D fluorescence lifetime imaging in deep tissues, showing it can accurately reconstruct intensity and lifetime data in scattering media.

PhenoStream: A Cyberinfrastructure for Automated and AI-Based Crop Trait Extraction from Aerial Imagery

PhenoStream automates crop trait extraction from aerial imagery, enabling high-throughput phenotyping with reduced latency and manual effort.

Decoupling Detection and Classification to Improve Morphological Phenotype Analysis of Sickle Red Blood Cells in Full-Scope Microscopy

This paper improves sickle cell disease morphology analysis by combining a YOLO detector with a DenseNet classifier for accurate detection and classification of red blood cell morphologies in full-scope microscopy images.

vFLIM: Machine Learning-enabled Light Sheet Fluorescence Lifetime Imaging

The paper introduces a machine learning-enabled pipeline for volumetric FLIM imaging in living systems, enabling long-term and high-speed data acquisition and analysis.

Self-blinking dye restores efficient use of nanobodies in single-molecule localization microscopy.

This paper demonstrates that self-blinking dye JF635b improves nanobody performance in single-molecule localization microscopy, enabling reliable imaging across multiple modalities with high precision.

Cell Cycle Phases, Spindle Dynamics and Kinesin-5 Motor LocalizationCharacterized by Deep Learning, Dual Segmentation and Decision-Tree Pipeline

The paper develops an automated image processing pipeline for analyzing yeast cell cycle dynamics using deep learning and dual segmentation, achieving faster analysis with minimal deviation from manual methods.

Label free multimodal optical imaging of metabolic heterogeneity and correlation in aging by integrating SRS, MPF, FLIM, and SHG

The paper introduces MANIFEST, a label-free imaging platform that integrates multiple optical modalities to analyze metabolic heterogeneity and spatial correlations in aging and disease models.

TipQUANT: A robust algorithm for quantitative analysis of spatiotemporally dynamic activities in tip-growing cells

TipQUANT quantifies spatiotemporal dynamics of fluorescent molecules in tip-growing cells, improving analysis efficiency and accuracy.