Cell Image Analysis Techniques

Deep Learning for Predicting Stem Cell Efficiency for use in Beta Cell Differentiation

This paper develops a deep learning model to predict stem cell efficiency for beta cell differentiation using phase-contrast images, achieving high accuracy in clone classification.

Fractal: Towards FAIR bioimage analysis at scale with OME-Zarr-native workflows

Fractal introduces a task specification and platform for scalable, reproducible OME-Zarr-native bioimage analysis workflows, demonstrating reproducibility across diverse imaging data and clinical settings.

Inverse Protocol Prediction from Spheroid Microscopy Imaging via Morphology-Aware Structured Learning

This paper introduces a method to infer experimental culture conditions from spheroid images using structured learning, achieving high accuracy in protocol reconstruction.

A high-throughput method for measuring fungal growth rate on solid media using automated imaging and deep learning

This paper introduces a high-throughput method for measuring fungal growth rates using automated imaging and deep learning, showing it is accurate and scalable.

A Modular In-Incubator Microscope for Longitudinal Live Cell Microscopy

This paper introduces an automated in-incubator microscope for long-term live cell imaging, enabling high-frequency, continuous data collection with improved thermal stability and operational reliability.

The One Click Wonder: a retrained automated segmentation pipeline that enables quantitative and modular analysis of C. elegans embryos

The paper introduces OCW, an automated segmentation pipeline for C. elegans embryos, and BAAM for single-cell quantitation, revealing distinct cell populations with varying transcriptional burst frequencies.

Modernizing histopathological analysis: a fully automated workflow for the digital image analysis of the intestinal microcolony survival assay

This paper introduces an automated workflow for analyzing histopathological images of the intestinal microcolony survival assay, achieving accuracy comparable to experts with reduced human error and time.

FishFeats: streamlined quantification of multimodal labeling at the single-cell level in 3D tissues

FishFeats provides a streamlined pipeline for quantifying multimodal labeling in 3D tissues, integrating image analysis steps into a user-friendly tool for non-computational users.

Light Microscopy-Based Organelle Quantification: A Comprehensive Protocol

This paper provides a comprehensive protocol for quantifying cellular organelles using light microscopy and Fiji/ImageJ, enabling precise, high-throughput, and statistically comparable measurements across different samples.

An Integrated and Configurable End-to-End Pipeline for Longitudinal Cell Painting Analysis

The paper introduces SCALE, a pipeline for longitudinal cell painting analysis, demonstrating its ability to handle imaging variability and enable consistent comparisons across time points.

Smart Microscopy: Current Implementations and a Roadmap for Interoperability

This paper reviews current smart microscopy implementations and proposes a framework for interoperability to improve collaboration and standardization in the field.

An automated image analysis pipeline for wide-field optical redox imaging of patient-derived cancer organoids

The paper develops an automated image analysis pipeline for wide-field optical redox imaging of patient-derived cancer organoids, achieving high accuracy and significantly faster processing than manual methods.

Anatolution, an online platform for consensus morphology

Anatolution provides an open-source platform for generating consensus-validated training data for cell-level morphometry in histology.

ML-guided robotic microinjection of single neurons in human brain organoids

This paper introduces a robotic system for high-throughput single-cell manipulation in human brain organoids using vision guidance.

MyoFuse: A fully AI-based workflow for automated quantification of skeletal muscle cell fusion in vitro

MyoFuse is an AI-based workflow that accurately quantifies the myogenic fusion index in skeletal muscle cells by improving nucleus segmentation and classification.

Deep Neural Networks to Register and Annotate Cells in Moving and Deforming Nervous Systems

This paper introduces deep neural networks for aligning and annotating cells in moving nervous systems, achieving high accuracy in registration and cell type identification.

A Python module for programmatic access to TrypTag genome-wide subcellular protein localisation data in Trypanosoma brucei

The paper introduces a Python module for accessing TrypTag data on protein subcellular localization in Trypanosoma brucei and demonstrates its use in analyzing age-based organelle composition differences.

Semi-automatic Geometrical Reconstruction and Analysis of Filopodia Dynamics in 4D Two-Photon Microscopy Images

The paper develops a semi-automatic workflow for tracing and tracking filopodia in 4D two-photon microscopy images using intensity data without segmentation.

Optimizing the Characterization and Quantification of Retinal Ganglion Cell Somas in Healthy and Injured Retinas Using Cellpose

The paper develops a custom Cellpose script and models to automate RGC soma quantification and characterization in retinas.

Multiplexed high-content imaging uncovers morphological diversity of lymphocyte activation and dysfunction

The paper introduces TGlow, a high-content imaging platform for single-cell phenotyping of human lymphocytes, revealing distinct morphological states and drug responses.

Integration and harmonization of cell shape images for generative modeling

This paper harmonizes 2D and 3D immunofluorescent cell shape datasets using generative AI to create synthetic cells for machine learning and in silico studies.

Deep learning enables quantitative subcellular analysis of plant-microbe interfaces

HFinder uses deep learning to automate and quantify analysis of plant-microbe interfaces in confocal images, enabling detailed study of subcellular interactions.

Resolving and Quantifying Viral-Like Particles via Blind Deconvolution

This paper introduces EpiVirQuant, a computer vision framework for automated enumeration and sizing of viral-like particles, showing 18% higher detection than manual counting.

An AI/ML-Powered Workflow for End-to-End Cell Line Development

This paper develops a deep learning tool for automating cell line development, achieving a 36-day workflow for generating high-titer antibody-producing cell lines.

BEADS: An Interactive Semi-Automated Workflow for 3D Fibrin Angiogenesis Assays Enabling Co-Culture and Directionality Analysis

BEADS provides a semi-automated image analysis pipeline for 3D fibrin angiogenesis assays, enabling co-culture and directionality analysis with improved efficiency and accuracy.

BlueNuclei: automated identification and classification of live and dead transfected neurons using interpretable features

BlueNuclei automates the identification and classification of live and dead transfected neurons using interpretable features, achieving near-human accuracy with faster processing than deep learning alternatives.

FAST: Filamentous Actin Segmentation Tool for quantifying cytoskeletal organization

FAST is a deep learning tool that segments and quantifies actin structures from microscopy images, improving efficiency and accuracy in studying cytoskeletal organization.

MCA: A Multicellular analysis Calcium Imaging toolbox for ImageJ

MCA is an ImageJ-based tool for analyzing calcium imaging data, validated on zebrafish datasets and applicable to multiple organisms and modalities.

Modeling 3D Mesoscaled Neuronal Complexity through Learning-based Dynamic Morphometric Convolution

This paper introduces a convolution framework for neuronal morphology reconstruction, showing improved performance on mesoscaled neuronal datasets.

SpatialDINO: A Self-Supervised 3D Vision Transformer that enables Segmentation and Tracking in Crowded Cellular Environments

SpatialDINO enables automated segmentation and tracking of crowded cellular objects using self-supervised 3D vision transformers without manual annotations or retraining.