Cell Image Analysis Techniques

The paper trains deep learning models to automatically classify cell division stages in nematode embryos using label-free microscopy, achieving high accuracy and reconstructing the cyclical progression of division.

The paper develops an AI tool for quantifying ultrastructural traits from 3D microscopic datasets and improves fixation protocols for plants.

This paper reviews methodological advancements in Cell Painting over a decade, highlighting improvements in protocols, feature extraction, and machine learning applications for biological insights.

The paper harmonizes whole-brain imaging datasets from multiple labs to enable automated neuron identification and identifies factors influencing neuron positioning in C. elegans.

Dimalis automates the analysis of bacterial cell growth from time-lapse imaging, providing standardized outputs for downstream processing.

This paper introduces asymmetric contrastive objectives for phenotypic screening, improving performance on benchmark datasets and real-world cell screening tasks.

The paper benchmarks cellular segmentation methods, showing Cellpose outperforms Mediar when tested fairly and a transformer-based variant of Cellpose does not improve performance.

This paper introduces a new dataset for cell detection in microscopy images using oriented bounding boxes and evaluates its effectiveness for cancer biology tasks.

This paper introduces flow zoometry, a high-throughput screening method for Drosophila, enabling rapid, automated, and high-resolution analysis of whole animals.

The paper introduces a MATLAB tool for quantitatively analyzing 2D tissue images, measuring morphology and co-localization parameters with flexibility across different tissue types.

Cellects quantifies cell expansion, motion, and morphology from 2D image data and time-lapse sequences, providing standardized outputs for statistical analysis.

Enso decodes bacterial cell cycle and cell type from static SMLM images using unsupervised deep learning without prior knowledge.

This study compares temporal deep learning models for classifying sequences of cell colony images to understand cellular state transitions.

The paper identifies two EB distribution patterns in Drosophila midguts and shows how Septate Junction proteins affect EB morphology and clustering during regeneration.

U-FISH improves FISH spot detection accuracy and generalization using a deep learning approach with a U-Net model and a large annotated dataset.

This paper develops a machine learning approach to classify 53BP1 foci in 2D images, achieving high performance with standard classifiers and a diverse parameter space.

This paper introduces VascuMap, a deep-learning tool for label-free microvessel segmentation and spatial analysis, achieving high accuracy and enabling comprehensive examination of vascular networks.

This study uses 3D CNN models to classify mitosis stages in 3D time-lapse cell sequences, achieving higher accuracy than previous methods.

The paper introduces a two-stage framework for detecting cell divisions in 2D and 3D time-lapse images using segmentation and regression models, achieving high accuracy with efficient architectures.

The paper introduces LYNSU, an automated method for neuropil segmentation in Drosophila brains using fluorescence images, achieving high accuracy without requiring image warping or alignment.

This paper introduces VR-SASE, a VR platform for dendritic spine analysis that outperforms existing tools and aligns with FAIR data principles.

QUAL-IF-AI is a deep learning tool that detects and manages artifacts in multiplex immunofluorescence images, achieving high accuracy and IoU across common artifact types.

This paper presents a digital imaging workflow for standardized, multiplexed quantitative analysis of adult Drosophila eye phenotypes, reducing variability and enabling precise morphometric comparisons.

The paper develops an optimized live imaging and analysis pipeline for tracking growth in Arabidopsis sepal epidermal layers.

The paper introduces a semi-automatic annotation tool for 2D and 3D image labelling to reduce expert effort in creating object-based labels.

This paper introduces pSAM, a method for analyzing high-plex immunofluorescence images, and shows it can generalize across different staining panels and tissues without manual annotation.

This paper introduces a deep learning method for accurate 3D chloroplast counting in plant cells, achieving over 95% accuracy compared to manual methods.

This paper uses deep learning to label 17 kidney microstructures in 3D fetal rhesus macaque kidneys, revealing spatial relationships between glomeruli, arteries, and nephrons.

This paper introduces a graph matching method to track neurons in freely-moving C. elegans, achieving over 75% accuracy with five labeled frames.

The paper introduces a contrastive analysis framework to disentangle perturbation effects from natural cell variation in optical pooled screens, showing improved performance in generating embeddings and reducing technical artifacts.