Cell Image Analysis Techniques

Machine learning driven image segmentation and shape clustering of algal microscopic images obtained from various water types

This study applies FastSAM for automated algal cell segmentation and clustering in water samples, showing high accuracy for single cells and varying clustering performance.

da_Tracker: Automated workflow for high throughput single cell and single phagosome tracking in infected cells.

The paper introduces da_Tracker, an automated workflow for high-throughput single-cell and single-phagosome tracking in infected cells, enabling quantitative analysis of pathogen-host interactions.

Spatio-temporal segmentation of contraction waves in the extra-embryonic membranes of the red flour beetle

The paper introduces an image analysis pipeline to segment, quantify, and visualize contraction waves in 2D+t and 3D+t microscopy data of Tribolium castaneum embryos, revealing novel dynamics in extra-embryonic membranes.

Deep Learning and 3D Imaging Reveal Whole-Body Alterations in Obesity

This paper introduces MouseMapper, a deep learning tool combined with 3D imaging to analyze whole-body structural changes in obesity, revealing nerve and immune system alterations.

Semi-Automatic Segmentation of Pseudomonas koreensis and Escherichia coli for Bacterial Growth Characterization

This paper introduces a semi-automatic segmentation method for bacterial colonies to improve growth characterization efficiency and accuracy.

Subcellular Region Morphology Reflects Cellular Identity

This study uses subcellular morphology and AI to classify cell types and predict molecular differences without relying on markers or destructive sampling.

Automated Cell Lineage Reconstruction using Label-Free 4D Microscopy

This paper introduces embGAN, a deep learning pipeline for automated cell detection and tracking in label-free 3D time-lapse imaging without manual annotation.

Caliban: Accurate cell tracking and lineage construction in live-cell imaging experiments with deep learning

Human-in-the-loop labeling of nuclear live-cell movies produced DynamicNuclearNet (over 2,621 divisions); Caliban, a tracker trained on it, beat four published trackers at division detection.

PyPlaque: an Open-source Python Package for Phenotypic Analysis of Virus Plaque Assays

PyPlaque is an open-source Python package for analyzing virus plaque assays, offering flexibility and modularity to improve measurement consistency and adaptability to different experimental setups.

A Postdoctoral Training Program in Bioimage Analysis

This paper describes a postdoctoral training program in bioimage analysis, highlighting its components, outcomes, and implications for training experts in professional skills.

Image Correlation Spectroscopy is a Robust Tool to Quantify Cellular DNA Damage Response

This paper introduces image correlation spectroscopy (ICS) as a method to quantify DNA damage response by analyzing protein clustering, outperforming traditional foci counting and enabling broader marker analysis.

Get to know your neighbors with a SNAQ™: A framework for single cell spatial neighborhood analysis in immunohistochemical images

The paper introduces SNAQTM, an R Markdown script for analyzing spatial neighborhoods in immunohistochemical images to study tumor-immune interactions.

A Multiparametric Analysis Reveals Differential Behavior of Spheroid Cultures on Distinct Ultra-Low Attachment Plates Types

This study compares spheroid formation and behavior across six ultra-low attachment plate types using four human cell lines, finding size and eccentricity differences that suggest plate type impacts experimental outcomes.

Biophysical Simulation Enables Multi-Scale Segmentation and Atlas Mapping for Top-Down Spatial Omics of the Nervous System

SiDoLa-NS uses synthetic images and multi-scale CNNs to achieve high-precision segmentation of nuclei and tissue structures in nervous system samples.

An arginine-rich nuclear localization signal (ArgiNLS) strategy for streamlined image segmentation of single-cells

The paper introduces ArgiNLS, a nuclear localization signal that improves image segmentation of single-cells by restricting fluorescent proteins to the nucleus, enhancing machine learning-based segmentation.

Accurate, fast and memory efficient quantification of immune cell phenotypes in cytometry using machine learning

The paper benchmarks machine learning algorithms for cytometry data analysis, finding decision trees provide high accuracy, speed, and memory efficiency for immune cell phenotype quantification.

Segmentation-free measurement of locomotor frequency in Caenorhabditis elegans using image invariants

This paper introduces an image invariant-based method to measure C. elegans locomotor frequency without segmentation, showing improved robustness to low image quality and background noise.

AutoMorFi: Automated Whole-image Morphometry in Fiji/ImageJ for Diverse Analyses and Discoveries

AutoMorFi automates whole-image morphometric analysis, generating over 47 measurements per cell quickly and enabling new discoveries across diverse biological samples.

A systematic evaluation of computational methods for cell segmentation

This study evaluates 18 cell segmentation methods on light microscopy and fluorescence images, finding attention-based models perform best and provides a pre-trained resource for segmentation.

Supervised Automation of Cell Counting in Confocal Microscopic Cochlear Imaging Datasets Using Macro in Imaris

The paper develops a supervised automation protocol in Imaris to streamline cell counting in confocal cochlear images, reducing manual effort and human error.

PAC-MAP: Proximity Adjusted Centroid Mapping for Accurate Detection of Nuclei in Dense 3D Cell Systems

PAC-MAP improves nucleus detection in dense 3D cell systems by predicting centroid positions and proximity, achieving high recall and F1 scores in spheroids and organoids.

HCS-3DX, a next-generation AI-driven automated 3D-oid high-content screening system

HCS-3DX is a new AI-driven system for 3D-oid high-content screening that improves resolution and single-cell analysis in 3D tumor models.

COLLAGE: COnsensus aLignment of muLtiplexing imAGEs

COLLAGE improves image alignment for multiplexed immunohistochemistry by breaking alignment into smaller puzzles, enabling parallel processing and reducing errors.

Virtual tissue microstructure reconstruction across species using generative deep learning

TiMiGNet reconstructs 3D tissue microstructure across species using GANs, enabling high-resolution predictions without paired images.

FInCH: FIJI plugin for automated and scalable whole-image analysis of protein expression and cell morphology

The paper introduces a Fiji plugin for automated whole-image analysis of protein expression and cell morphology across developmental stages in bird tissues.

Nanoconnectivity: a quantitative approach to examine the structural complexity of the brain

The paper introduces a tracing method to quantify brain connectivity by skeletonizing nerve fibers and analyzing synaptic contacts across different brain regions.

Automated, image-based quantification of peroxisome characteristics with perox-per-cell

perox-per-cell automates image-based quantification of peroxisome features in yeast cells, matching manual counts and enabling high-throughput analysis.

Label-Free Melanoma Phenotype Classification Using Artificial Intelligence-Based Morphological Profiling

This paper develops an AI-based classifier to classify melanoma cell phenotypes using morphology, validated by scRNA-Seq, showing morphology correlates with phenotypic states.

MitoStructSeg: mitochondrial structural complexity resolution via adaptive learning for cross-sample morphometric profiling

MitoStructSeg accurately segments mitochondrial structures and quantifies morphological features in human and mouse tissues using a deep learning framework.

Whole Tissue Imaging of Cellular Boundaries at Sub-Micron Resolutions for Automatic Cell Segmentation: Applications in Epithelial Bending of Ectodermal Appendages

This paper introduces a method for high-resolution whole tissue imaging and automatic cell segmentation, demonstrating its application in studying epithelial bending in transgenic mice and catsharks.