Cell Image Analysis Techniques

The paper uses electron microscopy data to classify cell types in the neocortex based on perisomatic features, showing that these features are sufficient for accurate classification even in large datasets.

NeuVue is a framework for managing and prioritizing proofreading tasks in connectomic datasets, enabling scalable and coordinated error correction by over forty proofreaders.

The paper introduces ZephIR, a semi-supervised multiple object tracking framework for 2D/3D videos, demonstrating its versatility in tracking biological systems.

This paper develops a machine learning pipeline for detecting bacteria in electron microscopy images of brain tissue, demonstrating its potential for large-scale analysis and quality control.

ArtSeg uses a deep learning model to automatically segment and remove artifacts from brightfield microscopy images using image-level labels, improving downstream analysis without requiring pixel-level annotations.

The paper introduces nAdder, a 3D scale metric for analyzing neuronal traces, providing a new way to describe and compare axonal and dendritic complexity.

The paper introduces Sholl descriptors for neuronal morphology analysis, demonstrating their effectiveness in clustering and classifying neurons across species and brain regions.

BOSS segments and tracks oligodendrocytes in 3D images over time with minimal human input, performing similarly to a blinded human observer.

The paper uses geometric deep learning to analyze 3D cell shapes from melanoma cells, identifying morphological signatures and predicting small molecule mechanisms.

This study predicts fluorescent Cell Painting images from brightfield images using deep learning, achieving high correlation and demonstrating potential for downstream analysis and repurposing imaging channels.

This paper introduces SynapseCLR, a contrastive learning method for 3D EM data to extract synapse features from mouse visual cortex, achieving high accuracy in synapse classification and annotation.

The BioImage Model Zoo provides a community standard, infrastructure, and open repository for sharing, testing, and deploying pre-trained deep learning microscopy models across multiple bioimage analysis tools.

NODeJ is an ImageJ plugin that automates 3D segmentation of nuclear objects, improving efficiency and reducing bias in analyzing chromatin organization.

The paper develops a cross-modality image alignment method to integrate EM and fluorescence imaging data in C. elegans, revealing dynamic cell behaviors during nervous system development.

This paper develops a machine learning framework for oocyte phenotyping using transmitted light images, identifying new features to assess maturation potential in mouse and human oocytes.

This paper introduces an automated alignment method and pipeline to create a more accurate 3D atlas of C. elegans neurons, improving neuron labeling confidence.

STracking is a Python library that streamlines particle tracking by combining algorithms into standardized pipelines, offering a user-friendly interface for analysis.

SimSearch enables fast ROI detection in microscopy images through user-guided deep learning with minimal interaction and prior information.

The paper explores the use of multiset similarity indices to quantify colocalization between concentrations, suggesting they can provide complementary information to Pearson correlation.

The paper develops a method to quantify 3D concentration gradients and cellular organisation in curved epithelial tissues using image processing and intensity correction.

This study benchmarks deep learning and feature engineering methods for analyzing high-content microscopy images to predict phenotypes and cluster cells without manual annotations.

Pytri is a system using multiple machine learning models to detect biological entities with higher accuracy than manual or OpenCV-based methods.

The paper introduces MorphoGNN, a graph neural network method for embedding single neuron morphology, achieving competitive performance in classification and retrieval tasks.

This paper introduces data-driven microscopy (DDM), a framework that enhances imaging fidelity by combining population-wide characterization with adaptive imaging techniques.

The paper introduces MorphoGraphX 2.0, a software tool that annotates developing organs with local coordinate systems to analyze gene expression and growth in spatial context.

GIANI is an open-source software for automated analysis of 3D microscopy images, enabling segmentation, morphology quantification, and protein expression analysis in various imaging contexts.

The paper compares segmentation methods for zebrafish brain vasculature, finds U-Net outperforms SegNet, and highlights the effectiveness of Sato enhancement and Otsu thresholding.

This paper introduces an automated method for quantifying chemotaxis index in C. elegans using liquid-handling robots and a custom image analysis tool.

This paper develops a deep learning method called RELIC to differentiate between normal and pathological kidney exfoliated cells using urine samples with 82% accuracy.

This paper benchmarks deep learning algorithms for 3D instance segmentation of confocal images, showing performance differences and advantages of end-to-end 3D methods.