Cell Image Analysis Techniques

NeuronID is a toolkit for automated processing of two-photon calcium imaging data, reducing over-segmentation and achieving near-human accuracy.

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

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

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

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 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.

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

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.

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.

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

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

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

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

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

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.

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

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

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

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

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

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

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

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

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

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