Cell Image Analysis Techniques

SKOOTS: Skeleton oriented object segmentation for mitochondria

SKOOTS segments mitochondria in 3D imaging data efficiently, revealing structural changes not detectable with existing methods.

ASTRA: a deep learning algorithm for fast semantic segmentation of large-scale astrocytic networks

ASTRA is a deep learning tool for automated segmentation of astrocytic calcium signals in two-photon imaging, outperforming existing methods and enabling large-scale analysis.

Automatic Classification of Normal and Abnormal Cell Division Using Deep Learning Analysis of Mitosis Videos

This paper develops a deep learning classifier to distinguish normal from abnormal cell division in mitosis videos, achieving high accuracy in classification.

NeuroWRAP: integrating, validating, and sharing neurodata analysis workflows

NeuroWRAP integrates and evaluates multiphoton calcium imaging analysis workflows to improve reproducibility and collaboration by combining existing algorithms and enabling custom workflows.

Cell Instance Segmentation via Multi-Scale Non-local Correlation

This paper introduces a method using non-local correlation in the embedding space to improve cell instance segmentation in EM images, reducing over- and under-segmentation issues.

BigNeuron: A resource to benchmark and predict best-performing algorithms for automated reconstruction of neuronal morphology

BigNeuron benchmarks automatic neuron reconstruction algorithms using gold standard annotations and image quality metrics, showing that consensus methods outperform individual algorithms.

Surface-guided computing to quantify dynamic interactions between cell morphology and molecular signals in 3D

The paper introduces u-Unwrap3D to map 3D cell surfaces and analyze interactions between geometry and molecular signals, revealing new dynamics in immune and tumor cell behaviors.

BetaBuddy: An end-to-end computer vision pipeline for the automated analysis of insulin secreting β-cells

BetaBuddy automates β-cell calcium spiking analysis using deep learning, improving accuracy and efficiency compared to manual methods.

Active mesh and neural network pipeline for cell aggregate segmentation

The paper introduces a segmentation pipeline combining neural networks and active meshes to segment and track cells in 3D time-lapse images of organoids and stem cells.

JUMP Cell Painting chemical dataset: Morphological map of 115,796 compounds in human cells

Cell Painting images and profiles for 115,796 compounds in U2OS cells from 12 laboratories; 15.2% of analyzed compounds are phenotypically active, and cross-laboratory replicates cut reference-compound activity by 34%.

IntestLine: a Shiny-based application to map the rolled intestinal tissue onto a line

IntestLine maps rolled intestinal tissue images onto a line to improve visualization and analysis of proximal-distal and serosa-luminal features.

Systematic analysis of cell morphodynamics in C. elegans early embryogenesis

This study systematically quantifies cell morphodynamics in C. elegans embryos, revealing high reproducibility and unique dynamics across cells.

A Weakly Supervised U-Net Model for Precise Whole Brain Immunolabeled Cell Detection

The paper develops a weakly supervised U-Net model for precise segmentation of whole brain immunolabeled cell signals, revealing distinct neural activity maps related to social motivations.

Nuclear instance segmentation and tracking for preimplantation mouse embryos

The paper introduces a dataset and method for nuclear segmentation and tracking in mouse embryos, identifying Stardist-3D as the most accurate method for this task.

Rapid and fully automated blood vasculature analysis in 3D light-sheet image volumes of different organs

VesselExpress automates 3D blood vasculature analysis in LSFM data, enabling rapid and unbiased quantification of vascular parameters across multiple organs.

Automated quantification of lipophagy in Saccharomyces cerevisiae from fluorescence and cryo-soft X-ray microscopy data using deep learning

The paper develops a deep learning method to quantify lipophagy in yeast using fluorescence and cryo-soft X-ray microscopy data.

A searchable image resource of Drosophila GAL4-driver expression patterns with single neuron resolution

The paper provides a searchable image resource of Drosophila GAL4-driver expression patterns at single neuron resolution to improve cell type identification and split-GAL4 combination prediction.

Small hand-designed convolutional neural networks outperform transfer learning in automated cell shape detection in confluent tissues

The paper develops optimized small CNNs for cell shape detection in confluent tissues, finding they outperform transfer learning in accuracy, speed, and simplicity.

petiteFinder: An automated computer vision tool to compute Petite colony frequencies in baker's yeast

petiteFinder is a deep learning tool that automates Petite colony frequency calculation in baker's yeast by analyzing images of Petri dishes.

GILEA: GAN Inversion-enabled latent eigenvalue analysis for phenome profiling and editing

GILEA uses GAN inversion and latent eigenvalue analysis to profile and edit phenomes from biomedical images, demonstrating its effectiveness with SARS-CoV-2 and skin lesion datasets.

MoBIE: A Fiji plugin for sharing and exploration of multi-modal cloud-hosted big image data

MoBIE is a Fiji plugin that enables visualization, sharing, and management of large, multi-modal image data across platforms.

Alignment, Segmentation and Neighborhood Analysis in Cyclic Immunohistochemistry Data Using CASSATT

CASSATT processes cycIHC data to align images, segment nuclei, and analyze spatial cell interactions and neighborhoods.

RxRx3: Phenomics Map of Biology

RxRx3 provides a large-scale high-content screening dataset combining CRISPR knockouts and small molecule treatments to enable machine learning research and biological discovery.

Fast4DReg: Fast registration of 4D microscopy datasets

Fast4DReg corrects drift in 3D microscopy datasets using 2D cross-correlations, outperforming existing tools in speed and accuracy.

Efficient image analysis for large-scale next generation histopathology using pAPRica

The paper introduces pAPRica, a framework for efficient analysis of large-scale histopathology images, achieving significant speedup and data reduction.

SUFI: An automated approach to spectral unmixing of fluorescent multiplex images captured in mouse and postmortem human brain tissues

SUFI automates spectral unmixing of fluorescent multiplex images in mouse and postmortem human brain tissues, improving accuracy over manual methods and existing software.

Self-supervised vision transformers accurately decode cellular state heterogeneity

This paper shows self-supervised vision transformers can accurately decode cellular state heterogeneity from microscopy data without prior training.

GliaMorph: A modular image analysis toolkit to quantify Müller glial cell morphology

GliaMorph quantifies Müller glial cell morphology across developmental and disease states, revealing structural changes and subcellular features in zebrafish and mouse models.

MorphoFeatures: unsupervised exploration of cell types, tissues and organs in volume electron microscopy

The paper introduces an unsupervised method to extract morphological features from 3D EM data, enabling cell type and tissue organization analysis in a segmented worm.

Fully automated detection of dendritic spines in 3D live cell imaging data using deep convolutional neural networks

This paper develops a deep learning pipeline for automated dendritic spine detection in 3D live cell imaging data, achieving near-human performance without retraining.