工作流程
软件部署
软件
Python(编程语言)
显微镜
通用串口总线
计算机科学
图像处理
数据采集
安装
显微镜
计算机硬件
芯(光纤)
嵌入式系统
软件工程
人工智能
系统工程
自动化
作者
Iván Hidalgo-Cenalmor,Ricardo Henriques,Estibaliz Gómez‐de‐Mariscal,Guillaume Jacquemet
摘要
Smart microscopy, in which real-time image analysis enables microscopes to adapt their acquisition dynamically, is becoming central to biological imaging. However, deploying such workflows in imaging facilities remains challenging. The computers that control microscopes in a facility are shared, governed by institutional policies, often offline and run vendor-specific software. As a result, installing custom software environments for the workflow of each user is both unsafe and unsustainable. We argue that smart microscopy requires a practical deployment strategy that links open-source bioimage analysis with commercial acquisition software without turning acquisition computers into development platforms. Using our recently developed tools, NucleiSky and LabConstrictor, as examples, we demonstrate how microscope control and external image analysis can be integrated while keeping the custom Python environment separate from the vendor-managed environment. The deployment strategy might vary - for instance, a USB drive, a facility workstation, a server or a network share - but the underlying principle remains the same: smart-microscopy workflows must be straightforward to approve, launch, reuse, audit and remove. Smart microscopy will become routine only when it is both powerful and deployable.
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