Cell Image Analysis Techniques

TickMotion measures and visualizes muscle contractions and movements in insect and arthropod tissues using pixel activity analysis from video recordings.

This paper introduces a morphology-appropriate representation learning framework for 3D multi-piece intracellular structures, showing improved sub-cluster discovery and phenotypic profiling.

This study uses a variational autoencoder to cluster nuclei on microgroove substrates, distinguishing between wildtype and laminopathy myoblasts based on nuclear deformation.

SwinCell is a 3D transformer-based framework that improves segmentation of dense cellular images by balancing local feature recognition and contextual understanding.

The paper introduces SReD, an unsupervised framework for detecting repetitive biological structures in microscopy images, demonstrating its use on three datasets.

This paper evaluates how background cells affect single-cell microscopy models and finds they reduce localization classification accuracy by up to 15.8%.

TransiScope automates and improves the analysis of transient events in time-lapse microscopy by optimizing parameters interactively, enhancing objectivity and reducing false positives.

This paper introduces a method for aligning blood vessel axes in CLEM images to improve registration accuracy.

The paper introduces interSeg, a deep learning tool that accurately predicts oncogene amplification status from interphase FISH images, achieving high accuracy in detecting ecDNA and chromosomal amplifications.

This study uses deep learning to classify transcriptomic subpopulations in breast cancer cell lines based on morphology, showing that chemotherapy-induced changes and intra-line subpopulations are identifiable.

EZ-FRCNN provides a user-friendly, offline deep learning tool for object detection in biological datasets, enabling applications from cell biology to ecological studies.

CloVarS generates synthetic single-cell lineage data to model time-lapse microscopy experiments and test biological hypotheses.

EyeHex automates segmentation of Drosophila compound eyes from images, providing accurate metrics on ommatidia structure across different strains and image types.

PB-scope uses deep learning to analyze PB dynamics and identifies drugs affecting JAK signaling pathway.

Segment AnyNeuron is an active learning framework for neuron segmentation using minimal annotations, achieving high precision on diverse neuron imaging datasets.

NTAC infers neuronal cell types from synaptic connectivity, achieving high accuracy with minimal labeled data and outperforming morphology-based methods.

Spotiflow improves spot detection in microscopy images using deep learning, achieving subpixel accuracy and efficiency across diverse datasets.

Cut-Detector automates the analysis of cytokinesis timing from time-lapse microscopy movies by detecting microtubule cuts in intercellular bridges.

This paper introduces STED-FM, a foundation model for STED microscopy that learns robust representations without extensive annotations, improving downstream tasks like classification, segmentation, and image denoising.

This paper introduces DEFORM-Net, a deep learning method for estimating full-field displacement in biological images, outperforming traditional and recent approaches in accuracy, speed, and spatial sampling.

This paper introduces a segmentation-free method to analyze live-cell imaging data of T cell-cancer cell interactions, revealing how T cell modifications affect aggregate formation dynamics.

Kaizen improves cell segmentation in microscopy images by using a VQ-VAE to iteratively refine object hypotheses.

This study uses a Variational Auto-Encoder to predict microalgal lipid content from label-free Imaging Flow Cytometry data, demonstrating a low-cost alternative to traditional methods.

The paper introduces a Bayesian optimization framework for automated registration of spatial expression data across modalities, achieving higher success rates on a large dataset of 954 image pairs.

This study quantifies morphodynamic features of peritoneal resident tissue macrophages in vivo to distinguish functional states and assess tissue age.

OmniSegger is an updated image analysis pipeline for bacterial cells that improves robustness to cell morphology and supports multiple imaging modalities, providing quantitative cellular analyses.

The paper introduces a benchmark and datasets for virus infection reporter virtual staining in microscopy, using U-Net and pix2pix models to address the challenge of continuous signal generation.

CRISP improves cell segmentation accuracy in dense calcium imaging data by refining user-drawn masks and automatically identifying cell outlines.

The paper uses Cell Painting to identify phenotypic effects of compounds in activated cells, revealing novel drug mechanisms and expanding phenotypic understanding.

Cell-TRACTR is a transformer-based model that segments and tracks cells in microscopy images, outperforming existing methods in tracking and division accuracy.