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

arXiv: https://arxiv.org/abs/2504.00047 · HTML: https://mlsanity.com/paper/2504.00047/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 21 of its 74 references that are in the map.
- First posted on arXiv 30 Mar 2025.

## Builds on (most cited first, 21 of 21)

- [Microsoft COCO: Common Objects in Context](https://mlsanity.com/paper/1405.0312/) (2014)
- [Segment Anything](https://mlsanity.com/paper/2304.02643/) (2023)
- [You Only Look Once: Unified, Real-Time Object Detection](https://mlsanity.com/paper/1506.02640/) (2015)
- [SAM 2: Segment Anything in Images and Videos](https://mlsanity.com/paper/2408.00714/) (2024)
- [Panoptic Segmentation](https://mlsanity.com/paper/1801.00868/) (2018)
- [Florence-2: Advancing a Unified Representation for a Variety of Vision Tasks](https://mlsanity.com/paper/2311.06242/) (2023)
- [Cell Detection with Star-convex Polygons](https://mlsanity.com/paper/1806.03535/) (2018)
- [Medical Image Segmentation Using Deep Learning: A Survey](https://mlsanity.com/paper/2009.13120/) (2020)
- [BiomedParse: a biomedical foundation model for image parsing of everything everywhere all at once](https://mlsanity.com/paper/2405.12971/) (2024)
- [4M-21: An Any-to-Any Vision Model for Tens of Tasks and Modalities](https://mlsanity.com/paper/2406.09406/) (2024)
- [Learning to Autofocus](https://mlsanity.com/paper/2004.12260/) (2020)
- [Contour Proposal Networks for Biomedical Instance Segmentation](https://mlsanity.com/paper/2104.03393/) (2021)
- [CFPNet-M: A Light-Weight Encoder-Decoder Based Network for Multimodal Biomedical Image Real-Time Segmentation](https://mlsanity.com/paper/2105.04075/) (2021)
- [Task Fingerprinting for Meta Learning in Biomedical Image Analysis](https://mlsanity.com/paper/2107.03949/) (2021)
- [Tracking one-in-a-million: Large-scale benchmark for microbial single-cell tracking with experiment-aware robustness metrics](https://mlsanity.com/paper/2411.00552/) (2024)
- [Autofocusing technologies for whole slide imaging and automated microscopy](https://mlsanity.com/paper/2006.08610/) (2020)
- [MLOps for Scarce Image Data: A Use Case in Microscopic Image Analysis](https://mlsanity.com/paper/2309.15521/) (2023)
- [Beyond Knowledge Silos: Task Fingerprinting for Democratization of Medical Imaging AI](https://mlsanity.com/paper/2412.08763/) (2024)
- [AI-based automated active learning for discovery of hidden dynamic processes: A use case in light microscopy](https://mlsanity.com/paper/2310.04461/) (2023)
- [Security Fence Inspection at Airports Using Object Detection](https://mlsanity.com/paper/2311.12064/) (2023)
- [EAP4EMSIG -- Experiment Automation Pipeline for Event-Driven Microscopy to Smart Microfluidic Single-Cells Analysis](https://mlsanity.com/paper/2411.05030/) (2024)

## Abstract (arXiv)

Microfluidic Live-Cell Imaging (MLCI) yields data on microbial cell factories. However, continuous acquisition is challenging as high-throughput experiments often lack real-time insights, delaying responses to stochastic events. We introduce three components in the Experiment Automation Pipeline for Event-Driven Microscopy to Smart Microfluidic Single-Cell Analysis (EAP4EMSIG): a fast, accurate Multi-Layer Perceptron (MLP)-based autofocusing method predicting the focus offset, an evaluation of real-time segmentation methods and a real-time data analysis dashboard. Our MLP-based autofocusing achieves a Mean Absolute Error (MAE) of 0.105 μm with inference times from 87 ms. Among eleven evaluated Deep Learning (DL) segmentation methods, Cellpose reached a Panoptic Quality (PQ) of 93.36 %, while a distance-based method was fastest (121 ms, Panoptic Quality 93.02 %).