让生物学家用自然语言完成显微图像分析，自动生成可复现的代码流程。
Agentic-J: An AI Agent for Biological Microscopy Image Analysis
- 基于多智能体系统，将自然语言指令转为可执行的ImageJ脚本。
- 支持从细胞分割到多条件定量的全流程分析，结果可追溯可共享。
- 适合不熟悉编程的生物学家快速开展复杂图像分析任务。
生物图像分析日益需要整合异构工具、编程环境与领域知识，而这些能力通常难以由单一研究者掌握。我们提出Agentic-J，一个容器化、多智能体的AI助手，主要面向ImageJ/Fiji平台，使生物学家能够以自然语言描述分析任务，如核分割、细胞追踪和多条件量化。该系统生成组织化的可执行脚本，并构建文档化项目结构，确保每一步分析决策可追溯，工作流可复现或共享。专用子智能体负责插件管理、代码生成、调试、质量保证和统计报告。本文介绍了系统的架构设计，演示了真实的生物显微图像分析工作流，并详细阐述了技术实现。
原文摘要 · Abstract (English)
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.
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