most citedacia-workflows: Automated Single-cell Imaging Analysis for Scalable and Deep Learning-based Live-cell Imaging Analysis Workflows

2 citations · 2 across the 3 of their papers we have counts for

collaborators

6 papers

cs.CV20252 cited

acia-workflows: Automated Single-cell Imaging Analysis for Scalable and Deep Learning-based Live-cell Imaging Analysis Workflows

Johannes Seiffarth, Keitaro Kasahara, Michelle Bund +7

Live-cell imaging (LCI) technology enables the detailed spatio-temporal characterization of living cells at the single-cell level, which is critical for advancing research in the l…

q-bio.QM2025

EAP4EMSIG -- Enhancing Event-Driven Microscopy for Microfluidic Single-Cell Analysis

Nils Friederich, Angelo Jovin Yamachui Sitcheu, Annika Nassal +12

Microfluidic Live-Cell Imaging (MLCI) yields data on microbial cell factories. However, continuous acquisition is challenging as high-throughput experiments often lack real-time in…

q-bio.QM2025

PyUAT: Open-source Python framework for efficient and scalable cell tracking

Johannes Seiffarth, Katharina Nöh

Tracking individual cells in live-cell imaging provides fundamental insights, inevitable for studying causes and consequences of phenotypic heterogeneity, responses to changing env…

cs.CV2025

How To Make Your Cell Tracker Say "I dunno!"

Richard D. Paul, Johannes Seiffarth, David Rügamer +2

Cell tracking is a key computational task in live-cell microscopy, but fully automated analysis of high-throughput imaging requires reliable and, thus, uncertainty-aware data analy…

q-bio.QM2024

EAP4EMSIG -- Experiment Automation Pipeline for Event-Driven Microscopy to Smart Microfluidic Single-Cells Analysis

Nils Friederich, Angelo Jovin Yamachui Sitcheu, Annika Nassal +12

Microfluidic Live-Cell Imaging (MLCI) generates high-quality data that allows biotechnologists to study cellular growth dynamics in detail. However, obtaining these continuous data…

cs.CV2024

Tracking one-in-a-million: Large-scale benchmark for microbial single-cell tracking with experiment-aware robustness metrics

J. Seiffarth, L. Blöbaum, R. D. Paul +6

Tracking the development of living cells in live-cell time-lapses reveals crucial insights into single-cell behavior and presents tremendous potential for biomedical and biotechnol…