Cell Image Analysis Techniques

Automated neuronal reconstruction with super-multicolour fluorescence imaging

This paper introduces a method for automated neuronal circuit reconstruction using super-multicolour fluorescence imaging and an unsupervised clustering algorithm.

Point set registration for combining fluorescence microscopy methods

The paper introduces a three-step point set registration method for aligning microscopy datasets, demonstrating its use in combining fluorescence and sequencing data.

A low bias method for quantification of Oil Red O staining

This paper introduces a low bias method for quantifying Oil Red O staining by using filter-based acquisition and background correction in image processing.

VASCilia (Vision Analysis StereoCilia): A Napari Plugin for Deep Learning-Based 3D Analysis of Cochlear Hair Cell Stereocilia Bundles

VASCilia is a Napari plugin that uses deep learning to analyze 3D cochlear hair cell stereocilia bundles, revealing biological insights about actin content and bundle organization.

A general method for bootstrapping dense 3D segmentations from sparse 2D annotations

This paper introduces a method to generate dense 3D segmentations from sparse 2D annotations, achieving accuracy comparable to dense annotations with significantly reduced human effort.

TLCellClassifier: Machine Learning Based Cell Classification for Bright-Field Time-Lapse Images

TLCellClassifier improves cell detection, tracking, and classification in time-lapse images using CNN, metric learning, and LSTM, outperforming existing tools in accuracy and efficiency.

A scalable and modular computational pipeline for axonal connectomics: automated tracing and assembly of axons across serial sections

The paper introduces a computational pipeline for automated tracing and assembly of axons across serial sections, validated on centimeter-scale data for orientation analysis and proofreading.

SpyDen: Automating molecular and structural analysis across spines and dendrites

SpyDen automates molecular and structural analysis in neural compartments by providing an open-source, customizable platform for image annotation and analysis.

RESPAN: A Deep Learning Pipeline for Accurate and Automated Restoration, Segmentation, and Quantification of Dendritic Spines

RESPAN improves dendritic spine quantification by integrating deep learning for restoration, segmentation, and analysis, achieving higher accuracy and usability than existing methods.

Piximi - An Images to Discovery web tool for bioimages and beyond

Piximi provides a no-programming web tool for biological image analysis using deep learning, enabling rapid insights without installation or coding.

DL4MicEverywhere: Deep learning for microscopy made flexible, shareable, and reproducible

DL4MicEverywhere provides a flexible, reproducible platform for deep learning in microscopy by using Docker containers and Jupyter notebooks to simplify access and deployment.

High-Resolution In Silico Painting with Generative Models

This paper introduces a simplified VQGAN training scheme for generating multi-channel organelle staining from bright field images, achieving top performance in a benchmark challenge.

Robust virtual staining of landmark organelles

The paper develops robust virtual staining methods for cellular landmarks using deep learning, showing improved performance across cell types and imaging conditions.

Self-supervised Vision Transformers for image-to-image labeling: a BiaPy solution to the LightMyCells Challenge

The paper develops self-supervised Vision Transformers for predicting fluorescence images from label-free transmitted light microscopy inputs, using the LightMyCells challenge as a benchmark.

2D Label-free Prediction of Multiple Organelles Across Different Transmitted-light Microscopy Images with Bag-of-Experts

This paper introduces a 'Bag-of-Experts' model for label-free prediction of multiple organelles across transmitted-light microscopy images, showing generalizability across imaging conditions.

EzReverse - a Web Application for Background Adjustment of Color Images

EzReverse is a web tool that adjusts the background of color images for better visualization, offering flexible inversion methods and multiple color space options.

Relationship Between Irregularities of the Nuclear Envelope and Mitochondria in HeLa cells Observed with Electron Microscopy

The paper quantifies the relationship between mitochondrial volume and nuclear envelope invaginations in HeLa cells using electron microscopy and compares segmentation algorithms.

A Distribution-aware Semi-Supervised Pipeline for Cost-effective Neuron Segmentation in Volume Electron Microscopy

This paper develops a distribution-aware semi-supervised pipeline to improve neuron segmentation in EM volumes by addressing distribution mismatch between labeled and unlabeled data.

micronuclAI: Automated quantification of micronuclei for assessment of chromosomal instability.

micronuclAI automates micronuclei quantification to assess chromosomal instability, achieving near-human performance on cancer cell line images.

Image-based profiling of healthy donor immune cells with Blood Cell Painting reveals novel genotype-phenotype associations

The paper develops Blood Cell Painting (BCP) to profile immune cell morphology in healthy donors, identifying genetic and clinical associations with immune cell features.

PCP Auto Count: A Novel Fiji/ImageJ plug-in for automated quantification of planar cell polarity and cell counting

PCP Auto Count is a Fiji/ImageJ plug-in that automates cell counting and planar cell polarity measurements, showing high accuracy across multiple tissues and species.

Deep learning-based reconstruction of embryonic cell-division cycle from label-free microscopy time-series of evolutionarily diverse nematodes

The paper trains deep learning models to automatically classify cell division stages in nematode embryos using label-free microscopy, achieving high accuracy and reconstructing the cyclical progression of division.

A connectomic study of a petascale fragment of human cerebral cortex

The paper presents a connectomic analysis of a human cerebral cortex fragment, revealing structural and connectivity features of neurons and synapses.

Finding and Following: A deep learning-based pipeline for tracking platelets during thrombus formation in vivo and ex vivo

This paper introduces a deep learning-based pipeline for tracking platelets in thrombus formation, showing faster in vivo clotting and less passive platelet movement compared to ex vivo conditions.

Anatomics MLT, an AI tool for large scale quantification of ultrastructural traits

The paper develops an AI tool for quantifying ultrastructural traits from 3D microscopic datasets and improves fixation protocols for plants.

A Decade in a Systematic Review: The Evolution and Impact of Cell Painting

This paper reviews methodological advancements in Cell Painting over a decade, highlighting improvements in protocols, feature extraction, and machine learning applications for biological insights.

Applying Cell Painting in Non-Tumorigenic Breast Cells to Understand Impacts of Common Chemical Exposures

The paper uses Cell Painting to assess the morphological effects of 16 chemicals and 21 small molecules on MCF10A cells, identifying shared and distinct features and linking some to Wnt pathway activation.

Unifying community-wide whole-brain imaging datasets enables robust automated neuron identification and reveals determinants of neuron positioning in C. elegans

The paper harmonizes whole-brain imaging datasets from multiple labs to enable automated neuron identification and identifies factors influencing neuron positioning in C. elegans.

Dimalis: A complete standalone pipeline to analyse prokaryotic cell growth from time-lapse imaging

Dimalis automates the analysis of bacterial cell growth from time-lapse imaging, providing standardized outputs for downstream processing.

Virtual reality empowered deep learning analysis of brain activity

DELiVR uses VR-generated data to train deep learning models for quantifying c-Fos in cleared mouse brains, revealing new activation patterns in cancer-related brain activity.