Bachelor Computer Science & Bioinformatics Improving Early S Phase Detection in Hoechst-Based Cell-Cycle Segmentation Using Temporal Context

Temporal dynamics improves machine learning-based prediction of cell state from quantitative phase imaging

The results show that dynamic information, rather than static morphology alone, drives accurate cell-state classification, and that 3D architectures are needed to fully exploit it for label-free dynamic phenotyping.

Cell Cycle Phases, Spindle Dynamics and Kinesin-5 Motor Localization Characterized by Deep Learning, Dual Segmentation and Decision-Tree Pipeline

An open-source Fiji pipeline for automated yeast cell image processing and deterministic classification of cell-cycle, spindle and protein dynamics is presented and demonstrates a 50-fold acceleration with ∼6% deviation from manual analysis.

Deep Learning-Based Imaging Informatics for Automated Detection of Cytoplasmic Strings in Embryo Time-Lapse Microscopy.

Automated Deep Learning Protocol for Quantifying Cell Viability in Live/Dead Fluorescence Microscopy.

A step-by-step protocol for automated cell viability quantification from two-channel live/dead fluorescence images using a deep learning pipeline built on a U-Net architecture with a ResNet-50 encoder pretrained on ImageNet, providing a reproducible framework adaptable to diverse cell types and imaging platforms.

Bacteria tracking and life cycle state classification using graph neural networks and pretrained vision transformers.

This work proposes a unified framework based on Graph Neural Networks for simultaneous bacteria tracking, division detection, and life cycle state classification and introduces a network-based approach for segmentation error correction using division and multi-frame correspondence predictions.

Detecting cell segmentation errors using doublet methods

In spatial transcriptomics, cell segmentation is used to draw boundaries around cells. Molecules located within a cell’s boundary are assigned to it, making its gene expression profile dependent on segmentation accuracy. To identify potentially problematic cells, studies increasingly use doublet detection methods, a cl...