Cell Image Analysis Techniques

Scalable Image-Based Quantification of Cell Permeability and Actin Remodeling: An Example in a Gut-on-Chip Platform

This paper introduces a scalable method using organ-on-chip technology and image analysis to quantify cell permeability and actin remodeling in response to toxic compounds.

Predicting cell cycle stage from 3D single-cell nuclear-stained images

CellCycleNet predicts cell cycle phases from 3D nuclear-stained images, outperforming SVM models and showing strong generalization across microscopes.

Integration of nuclear morphology and 3D imaging to profile cellular neighborhoods

The paper integrates 3D imaging with nuclear segmentation to analyze nuclear morphology in human tissue, revealing spatial patterns and interactions in disease progression.

Drug-induced cytotoxicity prediction in muscle cells, an application of the Cell Painting assay

The paper predicts drug-induced cytotoxicity in muscle cells using Cell Painting data from myoblasts, showing morphological similarities between statins and tyrosine kinase inhibitors.

DANEELpath: Open-source Digital analysis tools for histopathological research. Applications in NEuroblastoma modELs

DANEELpath is an open-source image analysis toolkit developed to study the effects of vitronectin and cilengitide in 3D-grown neuroblastoma cells using deep learning and spatial feature extraction.

Neuromorphic Imaging Cytometry on Human Blood Cells

This paper introduces a neuromorphic imaging cytometry system for human blood cells, achieving high accuracy with reduced resource usage.

Quantitative AI-based DNA fiber workflow to study replication stress

The paper introduces a quantitative AI-based DNA fiber workflow to analyze replication stress, demonstrating its efficiency and robustness through independent experimental datasets.

Suite3D: Volumetric cell detection for two-photon microscopy

Suite3D improves 3D cell detection in two-photon microscopy by correcting motion and segmenting cells across planes, enhancing detectability and signal quality.

Spatial and temporal signatures of cell competition revealed by K-function analysis

This study uses K-function analysis to quantify spatiotemporal dynamics of cell competition in MDCK co-cultures, revealing distinct mechanisms for ScrKD and RasV12 elimination.

Simulation and quantitative analysis of spatial centromere distributionpatterns

The paper develops a framework to quantitatively analyze spatial centromere distribution patterns using clustering metrics and modeling techniques, identifying Ripleys K Score as the most accurate metric.

Defocused reflectance imaging for low numerical aperture, large field of view quantitative live cell imaging studies

This paper demonstrates that controlled defocusing in low numerical aperture microscopes improves automated cell segmentation and tracking for high-throughput live-cell imaging.

Generative frame interpolation enhances tracking of biological objects in time-lapse microscopy

This paper shows that generative frame interpolation improves object tracking in time-lapse microscopy without requiring algorithm fine-tuning.

Automated Prediction of Fibroblast Phenotypes Using Mathematical Descriptors of Cellular Features

The paper develops mathematical descriptors to quantify fibroblast phenotypes and validates them using drug-treated cells to predict myofibroblast activation.

Enhanced Cell Tracking Using A GAN-based Super-Resolution Video-to-Video Time-Lapse Microscopy Generative Model

This paper introduces tGAN, a GAN-based model that generates synthetic time-lapse microscopy data to improve cell tracking by reducing reliance on manual annotations.

HortaCloud: An Open and Collaborative Platform for Whole Brain Neuronal Reconstructions

HortaCloud provides a cloud-based platform for collaborative whole-brain neuronal reconstruction from light microscopy data, enabling efficient analysis of large datasets without local hardware.

Computational analysis of morphological changes in Lactiplantibacillus plantarum under acidic stress

The paper develops a deep learning method to analyze cell morphology changes in Lactiplantibacillus plantarum under acidic stress and finds significant elongation at low pH.

Non-Invasive Quantification of Viability in Spheroids Using Deep Learning

This paper introduces NViR, a deep learning method for non-invasive viability quantification in spheroids, demonstrating its ability to predict drug-induced liver injury.

CABaNe, an automated, high content ImageJ macro for cell and neurite analysis

CABaNe is an ImageJ macro for automated cell and neurite analysis, showing improved speed and precision over manual methods in both small and large datasets.

A multi-modal whole-slide image processing pipeline for quantitative mapping of tissue architecture, histopathology, and tissue microenvironment

The paper introduces a multi-modal image processing pipeline for quantitative spatial analysis of tissue architecture, histopathology, and microenvironment using multi-scale imaging techniques.

DeepCristae, a CNN for the restoration of mitochondria cristae in live microscopy images

DeepCristae restores mitochondria cristae in low-resolution microscopy images using a CNN trained on STED data and specialized loss functions.

Supervised and unsupervised deep learning-based approaches for studying DNA replication spatiotemporal dynamics

This study uses supervised and unsupervised deep learning to analyze DNA replication dynamics in mouse cells, identifying altered patterns in Rif1-deficient cells and extending to patient samples.

Longitudinal tracking of neuronal activity from the same cells in the developing brain using Track2p

The paper introduces Track2p, a cell-tracking method for longitudinal analysis of neuronal activity in developing mouse brains, revealing a developmental transition in activity statistics.

Computational detection, characterization, and clustering of microglial cells in a mouse model of fat-induced postprandial hypothalamic inflammation

This paper introduces an automated algorithm to detect and cluster microglial cells in mouse hypothalamic tissue, revealing morphological subpopulations affected by high-fat diet.

deepthought: the microscopy acquisition stack as an object of study

The paper develops a microscopy acquisition stack and applies it to study DNA damage response without operator intervention.

Cecelia: a multifunctional image analysis toolbox for decoding spatial cellular interactions and behaviour

Cecelia provides a user-friendly image analysis toolbox for processing and analyzing microscopy data to study spatial cellular interactions and behavior.

LivecellX: Corrective Deep Learning for Object-Oriented Single-Cell Analysis in Live-Cell Imaging

LivecellX corrects segmentation and tracking errors in live-cell imaging using deep learning and provides tools for lineage reconstruction and dynamic analysis.

Stochastic Regression and Peak Delineation with Flow Cytometry Data

The paper introduces a method to separate signal and noise in flow cytometry data to improve genome copy number estimation in E. coli.

Artificial intelligence-enabled automated analysis of transmission electron micrographs to evaluate chemotherapy impact on mitochondrial morphology in triple negative breast cancer

The paper introduces an AI model to analyze mitochondrial morphology in TNBC using TEM images, finding significant structural changes in response to chemotherapy and metastasis.

DMC-BrainMap - an open-source, end-to-end tool for multi-feature brain mapping across species

DMC-BrainMap is an open-source tool for analyzing anatomical data across species, enabling streamlined processing, registration, segmentation, and visualization of brain data without programming expertise.

EASI-ORC: A Pipeline for the Efficient Analysis and Segmentation of smFISH Images for Organelle-RNA Colocalization Measurements in Yeast

EASI-ORC automates smFISH image analysis in yeast to measure RNA-organelle colocalization, offering a fast, accurate, and user-friendly solution.