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

• URL: http://arxiv.org/abs/2510.05886v2
• Date: Wed, 08 Oct 2025 07:05:37 GMT
• Title: acia-workflows: Automated Single-cell Imaging Analysis for Scalable and Deep Learning-based Live-cell Imaging Analysis Workflows
• Authors: Johannes Seiffarth, Keitaro Kasahara, Michelle Bund, Benita Lückel, Richard D. Paul, Matthias Pesch, Lennart Witting, Michael Bott, Dietrich Kohlheyer, Katharina Nöh,
• Abstract summary: Live-cell imaging (LCI) technology enables detailed characterization of living cells at the single-cell level.<n>High-temporal setups with tens to hundreds of parallel cell cultivations offer the potential for robust and reproducible insights.<n>Recent advances in deep learning methods for cell segmentation and tracking now enable the automated analysis of such large data volumes.
• Score: 0.786460153386845
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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 life sciences, from biomedical applications to bioprocessing. High-throughput setups with tens to hundreds of parallel cell cultivations offer the potential for robust and reproducible insights. However, these insights are obscured by the large amount of LCI data recorded per experiment. Recent advances in state-of-the-art deep learning methods for cell segmentation and tracking now enable the automated analysis of such large data volumes, offering unprecedented opportunities to systematically study single-cell dynamics. The next key challenge lies in integrating these powerful tools into accessible, flexible, and user-friendly workflows that support routine application in biological research. In this work, we present acia-workflows, a platform that combines three key components: (1) the Automated live-Cell Imaging Analysis (acia) Python library, which supports the modular design of image analysis pipelines offering eight deep learning segmentation and tracking approaches; (2) workflows that assemble the image analysis pipeline, its software dependencies, documentation, and visualizations into a single Jupyter Notebook, leading to accessible, reproducible and scalable analysis workflows; and (3) a collection of application workflows showcasing the analysis and customization capabilities in real-world applications. Specifically, we present three workflows to investigate various types of microfluidic LCI experiments ranging from growth rate comparisons to precise, minute-resolution quantitative analyses of individual dynamic cells responses to changing oxygen conditions. Our collection of more than ten application workflows is open source and publicly available at https://github.com/JuBiotech/acia-workflows.

Related papers

• Towards Agentic Intelligence for Materials Science [73.4576385477731]
  This survey advances a unique pipeline-centric view that spans from corpus curation and pretraining to goal-conditioned agents interfacing with simulation and experimental platforms.<n>To bridge communities and establish a shared frame of reference, we first present an integrated lens that aligns terminology, evaluation, and workflow stages across AI and materials science.
  arXiv  Detail & Related papers  (2026-01-29T23:48:43Z)
• Introduction to a Low-Cost AI-Powered GUI for Unstained Cell Culture Analysis [3.5092739016434567]
  This article presents a novel microscopy image analysis framework designed for low-budget labs equipped with a standard CPU desktop.<n>The Python-based program enables cytometric analysis of live, unstained cells in culture through an advanced computer vision and machine learning pipeline.<n>Its competitive segmentation speed on a CPU-based platform highlights its significant potential for basic research and clinical applications.
  arXiv  Detail & Related papers  (2025-09-14T17:12:17Z)
• CellVerse: Do Large Language Models Really Understand Cell Biology? [74.34984441715517]
  We introduce CellVerse, a unified language-centric question-answering benchmark that integrates four types of single-cell multi-omics data.<n>We systematically evaluate the performance across 14 open-source and closed-source LLMs ranging from 160M to 671B on CellVerse.
  arXiv  Detail & Related papers  (2025-05-09T06:47:23Z)
• Data Analysis in the Era of Generative AI [56.44807642944589]
  This paper explores the potential of AI-powered tools to reshape data analysis, focusing on design considerations and challenges. We explore how the emergence of large language and multimodal models offers new opportunities to enhance various stages of data analysis workflow. We then examine human-centered design principles that facilitate intuitive interactions, build user trust, and streamline the AI-assisted analysis workflow across multiple apps.
  arXiv  Detail & Related papers  (2024-09-27T06:31:03Z)
• PhagoStat a scalable and interpretable end to end framework for efficient quantification of cell phagocytosis in neurodegenerative disease studies [0.0]
  We introduce an end-to-end, scalable, and versatile real-time framework for quantifying and analyzing phagocytic activity. Our proposed pipeline is able to process large data-sets and includes a data quality verification module. We apply our pipeline to analyze microglial cell phagocytosis in FTD and obtain statistically reliable results.
  arXiv  Detail & Related papers  (2023-04-26T18:10:35Z)
• Organelle-specific segmentation, spatial analysis, and visualization of volume electron microscopy datasets [1.5332481598232222]
  Volume electron microscopy is the method of choice for the in-situ interrogation of cellular ultrastructure at the nanometer scale. Recent technical advances have led to a rapid increase in large raw image datasets that require computational strategies for segmentation and spatial analysis. We describe a practical and annotation-efficient pipeline for organelle-specific segmentation, spatial analysis, and visualization of large volume electron microscopy datasets.
  arXiv  Detail & Related papers  (2023-03-07T13:23:31Z)
• Deep Learning in Single-Cell Analysis [34.08722045363822]
  Single-cell technologies are revolutionizing the entire field of biology. Deep learning often demonstrates superior performance compared to traditional machine learning methods. This survey will serve as a reference for biologists and computer scientists, encouraging collaborations.
  arXiv  Detail & Related papers  (2022-10-22T08:26:41Z)
• CorpusBrain: Pre-train a Generative Retrieval Model for Knowledge-Intensive Language Tasks [62.22920673080208]
  Single-step generative model can dramatically simplify the search process and be optimized in end-to-end manner. We name the pre-trained generative retrieval model as CorpusBrain as all information about the corpus is encoded in its parameters without the need of constructing additional index.
  arXiv  Detail & Related papers  (2022-08-16T10:22:49Z)
• Distributed intelligence on the Edge-to-Cloud Continuum: A systematic literature review [62.997667081978825]
  This review aims at providing a comprehensive vision of the main state-of-the-art libraries and frameworks for machine learning and data analytics available today. The main simulation, emulation, deployment systems, and testbeds for experimental research on the Edge-to-Cloud Continuum available today are also surveyed.
  arXiv  Detail & Related papers  (2022-04-29T08:06:05Z)
• Towards an Automatic Analysis of CHO-K1 Suspension Growth in Microfluidic Single-cell Cultivation [63.94623495501023]
  We propose a novel Machine Learning architecture, which allows us to infuse a neural deep network with human-powered abstraction on the level of data. Specifically, we train a generative model simultaneously on natural and synthetic data, so that it learns a shared representation, from which a target variable, such as the cell count, can be reliably estimated.
  arXiv  Detail & Related papers  (2020-10-20T08:36:51Z)
• A System for Real-Time Interactive Analysis of Deep Learning Training [66.06880335222529]
  Currently available systems are limited to monitoring only the logged data that must be specified before the training process starts. We present a new system that enables users to perform interactive queries on live processes generating real-time information.
  arXiv  Detail & Related papers  (2020-01-05T11:33:31Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.