Application Tutorials
Contents:
- Timelapse of timelapses and z-stacks
- Using multi_d_acquistion_events
- Fast 3D + time imaging with hardware triggering
- Interactive Microscopy Control with ImJoy
- Denoising acquired images using deep learning
- Single-shot image-based autofocus with machine learning
- Targeted multi-contrast microscopy using attention-based multi-instance learning for tissue sections
- Using an external master clock for hardware control of a stage-scanning high NA oblique plane microscope
- Learned adaptive illumination multiphoton microscopy
- Streaming data from micro-manager to napari: PSF Viewer
- Timelapse of timelapses and z-stacks
This notebook shows how to repeatedly acquire a short time series and then a z stack at a set with a set delay in between.
- Using multi_d_acquistion_events
This notebook shows how to use the multi_d_acquisition_events function to automatically create events to use for acquistion.
- Fast 3D + time imaging with hardware triggering
This notebook acquires a fast TZYX data series. The camera is run at reduced ROI to achieve higher framerate (here 200 frames per second). Movement of the z stage is “sequenced” to speed up acquisition. The z stage advances to the next position in the sequence when a trigger from the camera is received. This eliminates delays due to software communication.
- Interactive Microscopy Control with ImJoy
This tutorial notebook shows how you can combine pycro-manager with ImJoy which is a web framework for building rich and powerful interactive analysis tools. Step by step, it shows how you can use ImJoy plugins for acquiring and visualzing images using a dedicated ImJoy plugin for Pycro-Manager in the notebook with snap/live buttons, exposure/binning controls and a full-featured device property browser. The built plugin can be hosted on Github, and use independently outside the Jupyter notebook interface.
- Denoising acquired images using deep learning
This tutorial demonstrates how to train a deep learning model for image denoising using data aquired by Pycro-Manager. This training is performed on Google Colab, which provides free usage of GPUs in the cloud, so no specialized hardware is required to implement it. The trained model will then be used to denoise images in real time using a Pycro-Manager image processor.
- Single-shot image-based autofocus with machine learning
This notebook shows how to use Pycro-Manager/Micro-Magellan to implement machine learning-powered, image-based autofocus, as detailed in this paper. It shows how to set up and use Micro-Magellan to collect training data, and how to use an acquisition hook to apply focus corrections during an experiment.
- Targeted multi-contrast microscopy using attention-based multi-instance learning for tissue sections
This tutorial shows how to use pycro-manager to perform analysis driven targeted multimodal/multiscale acquisition for automated collagen fiber-based biomarker identification. We will acquire brightfield images of a H&E stained cancer histology slide at 4x magnification, identify pathology relevant ROIs using a deep learning model based on the 4x image, and zoom into these ROIs to perform the collagen fiber-specific method of second-harmonic generation (SHG) laser scanning at 20x magnification. This allows for disease-relevant, collagen-specific features to be collected automatically and correlated with the gold standard H&E pathology method. We use Pycro-manager to read/write hardware properties (e.g. camera exposure, lamp intensity, turret position, stage position, etc.), change Micro-Manager hardware property configuration groups, acquire images and access the image data as NumPy array, and perform Z-stack acquisition via multi-dimension acquisition events.
- Using an external master clock for hardware control of a stage-scanning high NA oblique plane microscope
This tutorial shows how to setup Pycro-Manager to run a microscope that utilizes an external controller as the master clock. Specifically, this tutorial controls a light-sheet microscope where a sample with fluorescent labels is scanned at a constant speed through an oblique light sheet. The stage controller provides the TTL signals that ensure the camera is synchronized to the scanning stage. This approach makes use of post hardware and post camera hook functions built into Pycro-Manager. Using these hook functions, it is possible to rapidly build and acquire a multiple terabyte acquisition consisting of millions of images.
- Learned adaptive illumination multiphoton microscopy
This tutorial demonstrates how to implement Learned Adaptive Multiphoton Illumination microscopy using Pycro-Manager/Micro-Magellan. This technique enables automatic sample-dependent adjustment of excitation laser power in real time while imaging a sample on a 2-photon microscope in order to compensate for attenuation of fluorescence when imaging deep into intact tissue.
- Streaming data from micro-manager to napari: PSF Viewer
This tutorial shows how to acquire data using micromanager, then use pycro-manager to stream it to napari. Buttons to start and stop data acquisition are added to napari using the magic-gui package. In this example, the data displayed in napari resliced to get a live PSF viewer. However, reslicing is only a small example for the data analysis possible using napari.