Cell Image Analysis Techniques

Odon: An ultra-fast viewer for spatial proteomics

Odon is a fast, interactive viewer for spatial proteomics data that outperforms existing tools in loading and rendering large-scale imaging datasets.

Shape Factor Analysis as a Quantitative Framework for Assessing Spheroid and Organoid Morphology and Invasiveness

This paper develops a shape factor analysis method to quantify spheroid and organoid morphology, showing it provides more reliable morphology assessments than standard shape descriptors.

NucleiSky enables cross-scale multimodal registration of microscopy data using nuclei constellations

NucleiSky registers microscopy images across scales and modalities by aligning nuclei constellations, achieving accurate localization with minimal landmarks.

Penumbria: Advanced 3D cell segmentation for biomedical imaging

Penumbria improves 3D cell segmentation in biomedical imaging by addressing challenges like diverse morphologies and optical aberrations, achieving state-of-the-art accuracy across multiple datasets.

A pipeline for cell migration analysis in live-cell imaging data from human iPSC-derived forebrain assembloids.

Apical3DTip: Elliptic Cross-section-based Reconstruction for the Embryo Initial Cell of Arabidopsis

The paper introduces Apical3DTip, a method for 3D and 4D analysis of Arabidopsis embryo initial cell morphology using elliptic cross-sections and coordinate normalization, enabling accurate geometric characterization without mesh reconstruction.

Optical flow reveals motility signatures for inferring pathogenic bacterial mixture compositions via temporal convolutional networks

The paper uses temporal convolutional networks to classify bacterial mixture compositions from motion features in microscopy videos, achieving 93.3% accuracy without fluorescent labeling.

Mapping human microglial morphological diversity via handcrafted and deep learning-derived image features

The paper develops an imaging and analysis framework to map human microglial activation states using handcrafted and deep learning features, showing deep learning features achieve high classification accuracy and correlate with biological states.

InVitroGap: an open-source tool for automated quantification of wound closure in the in vitro scratch assay

InVitroGap is an open-source tool for automated quantification of wound closure in scratch assays, showing strong agreement with commercial software.

V3Cell: A Vision-Guided Virtual 3D Cell Framework for Phenotypic Modeling and Perturbation Prediction

V3Cell predicts organoid responses to chemical perturbations using non-invasive bright-field microscopy, creating virtual 3D cell models that mimic real organoids in structure and developmental dynamics.

Deep Transfer Learning for Dormancy and Outbreaking State Classification in Metastatic Breast Tumor Cells: A Benchmark of Modern Deep Learning Models

This study benchmarks deep learning models for classifying dormant versus proliferative breast tumor cells using spatiotemporal data from a 3D BME system, finding EfficientNet-B7 achieves high accuracy.

Modeling and Tracking of Heterogeneous Cell Populations via Open Multi-Agent Systems

This paper introduces an enhanced cell-tracking algorithm using open multi-agent systems to model and track heterogeneous cell populations in co-culture experiments, showing improved performance over existing methods.

Graphical and Interactive Spatial Proteomics Image Analysis Workflow

The paper introduces an interactive spatial proteomics workflow for analyzing imaging data, enabling reproducible and customizable analysis of protein expression and localization in tissues.

BioBrain: A Multi-Agent Framework for Natural Language Driven Quantitative Microscopy Data Analysis

BioBrain translates natural language into reproducible microscopy analysis pipelines, outperforming language models in accuracy while enabling non-experts to analyze complex imaging data.

SenoQuant: One-stop AI software for senescence marker analysis and prediction

SenoQuant is an AI software for analyzing and predicting senescence markers in tissues, offering spatial quantification across imaging modalities and supporting custom models.

Biom3d, a modular framework to host and develop 3D segmentation methods

Biom3d is a modular deep learning framework for 3D image segmentation that improves code sustainability and reproducibility.

MorphoStat: A Statistics-Aware Pipeline for Morphological Profiling Analysis

MorphoStat is a pipeline that normalizes and statistically analyzes morphological data from high-content imaging, recovering known mechanism-of-action classes in a benchmark dataset.

Looking beyond stereotyped neuron structures reveals links between beading and morphological rearrangements in aging phenotypes.

This study uses automated dendrite tracing and categorization to show that aging and cold shock cause distinct dendritic beading patterns driven by branching history and systemic effects, respectively.

PhenoBIC: operator-free single-cell spatial phenotyping in multiplex imaging data using deep learning of cell staining patterns

PhenoBIC is a deep learning model that automates cell phenotyping in multiplex imaging data, outperforming manual methods and other ML approaches with an F1-score of ~0.88.

Orion: Towards Lab Automation with Computer-Using Agents

Orion, an AI agent, automates biomedical image analysis by integrating large language models with software execution and reasoning, generating research reports and hypotheses from imaging data.

Impact of Regularization Methods and Outlier Removal on Unsupervised Sample Classification

This study evaluates how preprocessing techniques affect classification consistency in high-content assays, finding that non-repeatability is inherent and irreducible.

SCALLOPS: a scalable, integrated computational framework for Optical Pooled Screens

SCALLOPS improves processing of optical pooled screens by enabling efficient alignment and analysis of large-scale image data, demonstrating superior performance over existing methods.

CASC: Content-Aware Compaction of Sparse Microscopy Images

CASC reduces microscopy image file sizes by removing spatial redundancy while preserving object-level biological structures.

A Computational Framework for Extracting Mechanistic Hypotheses from Quantitative Data of Morphological Dynamics

The paper introduces a computational framework to extract mechanistic hypotheses from morphological dynamics data by analyzing correlations and gene perturbations in C. elegans embryos.

PixelDeck: A local-first media library manager for biomedical imaging

PixelDeck organizes and manages large biomedical image and video libraries with duplicate detection and metadata extraction, enabling efficient curation and preparation for downstream analysis.

A quantitative coordinate system for developmental dynamics

This paper introduces STERN, a framework that quantitatively compares developmental trajectories across embryos by learning spatiotemporal representations from 4D imaging data.

ConnectoFM: A Foundation Model for Learning the Language of the Connectome

ConnectoFM is a foundation model for connectomics that outperforms existing methods in low-data regimes and improves accuracy for critical biological structures.

Cytomove: a browser-local and reviewable workflow for scratch wound healing assay quantification

Cytomove provides a browser-based tool for quantifying scratch wound healing assays with reviewable segmentation and export of analysis results.

FLAG-X: Hybrid machine learning workflows for automated gating of clinical flow cytometry data

FLAG-X provides a Python package with hybrid workflows for automated flow cytometry gating, integrating manual and machine learning methods to improve consistency and efficiency in clinical settings.

Chromatic topological mapping reveals organelle-specific spatial organization within microglia

The paper introduces a method to analyze organelle spatial organization in branched cells, finding distinct spatial programs in microglia under different conditions.