Agentic-J: An AI Agent for Biological Microscopy Image Analysis
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arXiv
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| Autori principali: | , , , , , , , , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2026
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| _version_ | 1866916074243489792 |
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| author | Johanns, Lukas Moor, Marilin Panzeri, Davide Zhou, Yu Chen, Xinyi Pauly, Nora F. K. Pan, Zixuan Gunzer, Matthias Müller, Andreas Shi, Yiyu Peterson, Hedi Chen, Jianxu |
| author_facet | Johanns, Lukas Moor, Marilin Panzeri, Davide Zhou, Yu Chen, Xinyi Pauly, Nora F. K. Pan, Zixuan Gunzer, Matthias Müller, Andreas Shi, Yiyu Peterson, Hedi Chen, Jianxu |
| contents | Biological image analysis increasingly demands integration across heterogeneous tools, programming environments, and domain knowledge that few researchers can command simultaneously. We present Agentic-J, a containerised, multi-agent AI assistant, primarily for ImageJ/Fiji that enables biologists to specify analysis tasks in natural language, from nuclei segmentation and cell tracking to multi-condition quantification. The agent generates executable scripts organised into a documented project structure, so every analysis decision is traceable and the workflow can be reproduced or shared. The specialised sub-agents handle plugin management, code generation, debugging, quality assurance, and statistical reporting. In this paper we introduce the system's design, demonstrate real biological microscopy image analysis workflows, and detailed the technical implementation. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2606_02080 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Agentic-J: An AI Agent for Biological Microscopy Image Analysis Johanns, Lukas Moor, Marilin Panzeri, Davide Zhou, Yu Chen, Xinyi Pauly, Nora F. K. Pan, Zixuan Gunzer, Matthias Müller, Andreas Shi, Yiyu Peterson, Hedi Chen, Jianxu Multiagent Systems Artificial Intelligence Computer Vision and Pattern Recognition Biological image analysis increasingly demands integration across heterogeneous tools, programming environments, and domain knowledge that few researchers can command simultaneously. We present Agentic-J, a containerised, multi-agent AI assistant, primarily for ImageJ/Fiji that enables biologists to specify analysis tasks in natural language, from nuclei segmentation and cell tracking to multi-condition quantification. The agent generates executable scripts organised into a documented project structure, so every analysis decision is traceable and the workflow can be reproduced or shared. The specialised sub-agents handle plugin management, code generation, debugging, quality assurance, and statistical reporting. In this paper we introduce the system's design, demonstrate real biological microscopy image analysis workflows, and detailed the technical implementation. |
| title | Agentic-J: An AI Agent for Biological Microscopy Image Analysis |
| topic | Multiagent Systems Artificial Intelligence Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2606.02080 |