Friedrich Miescher Institute for Biomedical Research, Basel, Switzerland.
Faculty of Natural Sciences, University of Basel, Basel, Switzerland.
Front Neural Circuits. 2018 Jul 31;12:54. doi: 10.3389/fncir.2018.00054. eCollection 2018.
We present , an open-source Python-based application to operate serial block-face electron microscopy (SBEM) systems. is designed for complex, challenging acquisition tasks, such as large-scale volume imaging of neuronal tissue or other biological ultrastructure. Advanced monitoring, process control, and error handling capabilities improve reliability, speed, and quality of acquisitions. Debris detection, autofocus, real-time image inspection, and various other quality control features minimize the risk of data loss during long-term acquisitions. Adaptive tile selection allows for efficient imaging of large tissue volumes of arbitrary shape. The software's graphical user interface is optimized for remote operation. In its user-friendly viewport, tile grids covering the region of interest to be acquired are overlaid on previously acquired overview images of the sample surface. Images from other sources, e.g., light microscopes, can be imported and superimposed. complements existing installations on 3View systems but permits higher acquisition rates by interacting directly with the microscope's control software. Its modular architecture and the use of Python/PyQt make highly customizable and extensible, which allows for fast prototyping and will permit adaptation to a wide range of SBEM systems and applications.
我们展示了一个基于 Python 的开源应用程序，用于操作串行块面电子显微镜（SBEM）系统。 是为复杂、具有挑战性的采集任务而设计的，例如神经元组织或其他生物超微结构的大规模体积成像。先进的监测、过程控制和错误处理功能可提高采集的可靠性、速度和质量。碎片检测、自动对焦、实时图像检查和其他各种质量控制功能可最大程度地降低长期采集过程中数据丢失的风险。自适应图块选择可实现任意形状大组织体积的高效成像。该软件的图形用户界面经过了远程操作的优化。在其用户友好的视口中，要采集的感兴趣区域的图块网格覆盖在样本表面的先前采集的概述图像上。可以导入来自其他来源（例如，光学显微镜）的图像并将其叠加。 补充了 3View 系统上现有的 安装，但通过直接与显微镜的控制软件交互，可实现更高的采集速度。其模块化架构和 Python/PyQt 的使用使 具有高度的可定制性和可扩展性，这允许快速原型制作，并将适应广泛的 SBEM 系统和应用。
