This code repository accompanies the paper "Autofocusing optical microscope using artificial neural network for large-area, high-magnification scanning" by Shizhao Lu and Evan MacBride. This project was prepared for the CHEG/CISC 867 course at the University of Delaware in Spring 2022. Our project mentor was Dr. Houk Jang of Brookhaven National Laboratory.
Microscopy imaging can provide structural information important for elucidating structure-property relationships of soft materials. The effectiveness of an instrument’s autofocus method is a critical consideration during microscopy imaging. Delicate control of a microscope to quickly move to the focal plane is essential for timely, high quality, highly magnified images over a large area. Traditional iterative autofocus methods are time consuming, while deep learning methods have been broadly adopted for quick and automatic analysis of images for various image learning tasks. In this report, we develop a machine learning workflow automating the prediction of the distance to the focal plane of microscopy images. Incorporating edge detection and transfer learning, our machine learning workflow achieves rapid, highly accurate prediction on images containing metal patterns with little computational resource requirement.
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| An out-of-focus microscope image from the level 1 dataset |
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| The machine learning workflow for the project |
For a walkthrough of our machine learning workflow, open Walkthrough.ipynb (found in the notebooks folder) and click “Open in Colab.” Follow the directions given in the notebook. Additionally, all notebooks used to create the project, refine preprocessing methods, take performance measurements, etc. are available in the notebook archive. Each of these notebooks can also easily be launched in Google Colab by clicking the "Open in Colab" badge. Change to the new Google Colab and select "Runtime > Run all" to run the notebook.
If you wish to use the preprocessing methods from this project in your own code, you can import the preprocessing.py module from the tools folder. Docstrings are provided for each function for your convenience. A quick run-through of preprocessing.py's functions are given in preprocessing_demo.py. You can modify preprocessing_demo.py as needed to incorporate our preprocessing workflow into your own project.
You will need some microscopy images to train and test the models. Sample microscopy images provided by Dr. Houk Jang are available in the sample_data folder. To easily use the sample_data images in the Walkthrough notebook from within Google Colab, you can copy the images to a directory in your Google Drive. The results shown in the Walkthrough notebook were generated with images from the “level 1” folder in sample_data.
Saved, trained regressor models are available in tools/resources. A quick demo of loading saved models to perform regression tasks (load_saved_demo.py) is available in tools.
For more information on this project, including numerous performance plots, schematics, and illustrations, you can view a PDF of the project's final presentation in the docs folder.