Cell Image Analysis Techniques

AimSeg: a machine-learning-aided tool for axon, inner tongue and myelin segmentation

AimSeg is a machine learning tool for segmenting axons, inner tongue, and myelin in EM images, improving efficiency and accuracy over manual methods.

A Motion Transformer for Single Particle Tracking in Fluorescence Microscopy Images

This paper introduces a Transformer-based method for single particle tracking in fluorescence microscopy images, showing improved performance over existing methods.

Cell Segmentation in Images Without Structural Fluorescent Labels

This paper presents a segmentation workflow that enables cell segmentation without structural fluorescent labels, improving performance and robustness across cell lines and fluorescent labels.

Visualization & Quality Control Tools for Large-scale Multiplex Tissue Analysis in TissUUmaps 3

The paper introduces three plugins for visualization and quality control in large-scale multiplex tissue analysis to improve accuracy and user trust in automated approaches.

Cell Painting transfer increases screening hit rate

The paper uses Cell Painting images to preselect compounds for drug screening, significantly increasing hit rates by reducing the number of compounds tested.

Weakly-supervised temporal segmentation of cell-cycle stages with center-cell focus using Recurrent Neural Networks

This paper develops a weakly-supervised method using recurrent neural networks to classify cell-cycle stages in mitosis videos without annotated data, achieving performance comparable to supervised approaches.

Deep Representation Learning Determines Drug Mechanism of Action from Cell Painting Images

The paper introduces MOAProfiler, a deep learning encoder that improves the accuracy of drug mechanism of action classification from Cell Painting images compared to traditional and existing deep learning methods.

Local Shape Descriptors for Neuron Segmentation

This paper introduces an auxiliary learning task using Local Shape Descriptors to improve neuron segmentation in electron microscopy volumes, achieving performance comparable to state-of-the-art methods with greater efficiency.

Fine-grained, nonlinear image registration of live cell movies reveals spatiotemporal organization of diffuse molecular processes

This paper develops a nonlinear image registration method for live cell movies to analyze spatiotemporal dynamics of diffuse molecular processes, revealing organized Profilin dynamics linked to actin organization.

SpheroScan: A User-Friendly Deep Learning Tool for Spheroid Image Analysis

SpheroScan is a deep learning tool for automated spheroid image analysis that improves reproducibility and throughput in 3D spheroid assays.

An ImageJ-based tool for three-dimensional registration between different types of microscopic images

The paper introduces an ImageJ-based tool for 3D registration between microscopic images, enabling alignment and reconstruction of 3D rotated tissues like mouse embryos and organoids.

FriendlyClearMap: An optimized toolkit for mouse brain mapping and analysis

FriendlyClearMap simplifies and extends the ClearMap pipeline for mouse brain analysis, enabling efficient and scalable neuroanatomical studies.

Vessel Metrics: A python based software tool for automated analysis of vascular structure in confocal imaging

Vessel Metrics is a software tool that automates vascular structure analysis in confocal images, improving consistency and efficiency across research groups.

STrack: A tool to Simply Track bacterial cells in microscopy time-lapse images

STrack is a tool for tracking bacterial cells in time-lapse microscopy images, showing high consistency across six bacterial strains.

TACI: an ImageJ plugin for 3D calcium imaging analysis

The paper introduces TACI, an ImageJ plugin for analyzing 3D calcium imaging data, improving reproducibility and reducing human bias without requiring coding.

Evaluation of cell segmentation methods without reference segmentations

The paper evaluates cell segmentation methods using automated metrics instead of human reference segmentations, finding two deep learning methods perform best with improved results via postprocessing.

A Scalable 3D High-Content Imaging Protocol for Measuring a Drug Induced DNA Damage Response Using Immunofluorescent Sub-nuclear γH2AX Spots in Patient Derived Ovarian Cancer Organoids

This paper develops a scalable 3D imaging protocol to measure DNA damage response in ovarian cancer organoids using γH2AX staining and shows its effectiveness with Etoposide treatment.

LapTrack: Linear assignment particle tracking with tunable metrics

LapTrack improves particle tracking by solving a linear assignment problem with customizable metrics and parallel parameter tuning, enhancing tracking accuracy on real and artificial datasets.

A systematic review and comparison of automated tools for quantification of fibrous networks

This study compares automated tools for quantifying fibrous network structures in images, finding that tools reliably measure relative changes but not absolute values.

Learning orientation-invariant representations enables accurate and robust morphologic profiling of cells and organelles

This paper introduces O2-VAE, an orientation-invariant representation learning method for morphologic profiling of cells and organelles, improving clustering and outlier detection.

COMBINe: Automated Detection and Classification of Neurons and Astrocytes in Tissue Cleared Mouse Brains

COMBINe automates detection and classification of neurons and astrocytes in cleared mouse brains using RetinaNet to analyze MADM-based genetic modifications.

CaPTure: Calcium Peak Toolbox for analysis of in vitro calcium imaging data

CaPTure is a scalable pipeline for analyzing calcium imaging data from cultured neurons, enabling high-throughput phenotypic discovery.

microbeSEG: A deep learning software tool with OMERO data management for efficient and accurate cell segmentation

microbeSEG provides a deep learning tool for automated cell segmentation in microbial cultures, achieving accurate results within 45 minutes of user time.

UnMICST: Deep learning with real augmentation for robust segmentation of highly multiplexed images of human tissues

The paper introduces two methods to improve tissue segmentation in highly multiplexed images using real augmentations and nuclear envelope imaging, resulting in more accurate models.

RS-FISH: Precise, interactive, fast, and scalable FISH spot detection

RS-FISH detects FISH spots accurately and efficiently in 2D and 3D images, supporting applications like smFISH and spatial genomics.

Multiplexed and reproducible high content screening of live and fixed cells using the Dye Drop method

The paper introduces a method called Dye Drop for high-throughput single-cell assays in 384-well plates, enabling detailed dose-response analysis of small molecules in breast cancer cell lines.

Digitally Predicting Protein Localization and Manipulating Protein Activity in Fluorescence Images Using Four-dimensional Reslicing GAN

The paper introduces a GAN-based method to predict protein localization in fluorescence images and manipulate protein activity to study localization-based functions.

Using conditional Generative Adversarial Networks (GAN) to generate de novo synthetic cell nuclei for training machine learning-based image segmentation

The paper uses GANs to generate synthetic cell nuclei and masks for training image segmentation models, demonstrating effectiveness with small datasets.

Machine learning-based detection of label-free cancer stem-like cell fate

This study develops a deep learning algorithm for analyzing label-free brightfield microscopy images to detect cancer stem-like cell fate, showing improved accuracy and speed compared to other methods.

Topology-based fluorescence image analysis for automated cell identification and segmentation

This paper introduces TOBLERONE, a topological image analysis tool for automated cell segmentation and membrane order quantification using confocal microscopy images.