pyCHIP is a tool for image segmentation and feature classification in transmission electron microscopy (TEM) images based on a small support set of user-provided examples. It is based on a few-shot machine learning approach, which can classify a variety of microstructural features from limited examples.

pyCHIP is most useful for trained microscopists who would like to identify features in their TEM images, where such an analysis would ordinarily be tedious or time-consuming by hand.

This code is the result of a project for the University of Washington Data Intensive Research Enabling Clean Technologies (UW-DIRECT) capstone program. For more information on the program, visit: https://depts.washington.edu/uwdirect/

pyCHIP usage occurs into four steps: Data Import, Grid Selection and Chipping, Support Set Selection, and Classification Display. This release of the code does not include the proprietary few-shot module needed to process data, but does include all other GUI elements. More details are given in the associated manuscript.

Begin by clicking on Analyze and Predict in the menu on the left.

In this step, the user selects data to be processed. Currently allowed data formats are jpg, png, tiff, and dm4. The first three are common image formats, while the last is a proprietary container format used by the Gatan Microscopy Suite, an industry-standard TEM analysis application. Images should be imported without any visible scale bar or other annotation. If no image is uploaded before submitting, an error message indicates that no appropriate file was selected. While in principle any image resolution can be used, the processing time does increase significantly with larger resolution. For best performance, it is recommended to downsample large images to resolution of 512 x 512 or 1024 x 1024 pixels. However, it is important to avoid aliasing artifacts, which can occur in high-resolution micrographs, particularly those containing lattice features.

Now the user must perform the important task of grid size selection and image chipping. Put simply, the user selects an appropriate sampling grid relative to their features of interest, which is used to chip the image in the backend few-shot code. To aid in this process, the user can drag a slider bar to dynamically adjust a superimposed grid atop their image. An ideal support set for the few-shot pyCHIP model is built from a grid with each square containing a unique microstructural feature or motif. The range of allowed grid sizes is dynamic, scaling from a minimum 50 pixels up to the dimensions of the uploaded image. The default lower limit is set to 50 pixels for visibility, but can be as small as the single pixel level. Too coarse a grid size will not capture relevant features, while too fine a grid will greatly increase processing time, so the user should test different sizes to strike an optimal balance. Once an appropriate size is determined, the user clicks the "Use This Grid Size" button and the desired grid size is passed to a function that crops the edges of the full size image to the total mesh, sub-divides the image, and saves the resulting collection of support set "chips." Both the raw chips and support sets are saved for future recall.

After chipping, the user must assign support set examples as features of interest. Building on the concept of the ubiquitous CAPTCHA (Completely Automated Public Turing test to tell Computers and Humans Apart) security feature on websites, we have designed an intuitive interface for labeling. The chips created in the previous step now appear on the dashboard as interactive objects. The user is provided with an empty starting support set in a drop-down menu, which can be renamed. This menu controls the specific support set(s) that is/are available for modification. Any image chips that the user clicks on will be added to the currently staged support set and appear in the right panel. These selected chips will appear highlighted in colors based on the user-designated support set. In cases where the user clicks the wrong chip and would like to remove it from the support set, they may click the highlighted chip again. Additional support sets can be added or removed by clicking the appropriate buttons at the top of the panel. In practice, the user is is advised to assign a minimum of 2 chips or maximum of 10 chips to each labeled set. These chips should be selected from different parts of the image to capture some of the variability in the data. Since the selected chips will become the support set for the few-shot ML model, all suspected feature types should be selected—sets can easily be adjusted by reprocessing. After the chip selection and labeling, the user may click "Save Support Set & Process" to update the training set and begin training the model. The trained model will then predict features against the selected support sets.

After training the model using the selected chips, the model predicts the support set label for all the chips in the original image and returns a colorized segmented image. The right side presents the count for each user-selected support set within the image. The segmented image and associated statistics tell the user critical information regarding features in the image. The user can export the colorized image (png format), the bar chart (png or svg formats), and the raw data for the bar chart (csv format).

cd <replace with location of project folder>
git clone https://github.com/pnnl/pychip_gui.git
cd pychip_gui

#if have Mac:

python3 -m venv env
source env/bin/activate


#if have Windows:

virtualenv env
\path\to\env\Scripts\activate

cd prototype
pip install -r requirements.txt

python app.py

- go to https://127.0.0.1:5000/

cd to pychip_gui
git pull

#reactivate environment
source env/bin/activate #if have mac
\path\to\env\Scripts\activate #if have windows


cd to prototype folder
python app.py

- For ease of use a demo video has been included in the repository under /docs/videos/UIDemo
- Additional Instructions have been included in the Prototype app.

If you find this application useful and want to publish your results, please cite the following:

• Doty, C., Gallagher, S., Cui, W., Chen, W., Bhushan, S., Oostrom, M., Akers, S., & Spurgeon, S. R. (2022). Design of a Graphical User Interface for Few-Shot Machine Learning Classification of Electron Microscopy Data. Computational Materials Science, 203, 111121. [https://doi.org/10.1016/j.commatsci.2021.111121]

• Akers, S., Kautz, E., Trevino-Gavito, A., Olszta, M., Matthews, B. E., Wang, L., Du, Y., & Spurgeon, S. R. (2021). Rapid and flexible segmentation of electron microscopy data using few-shot machine learning. npj Computational Materials, 7(1), 187. [https://doi.org/10.1038/s41524-021-00652-z]

For questions about pyCHIP usage or the few-shot machine learning approach, contact Steven Spurgeon ([email protected]).

University of Washington DIRECT Capstone Program Students: Christina Doty, Shaun Gallagher, Wenqi Cui, Shweta Bhushan and Wenya Chen

Pacific Northwest National Laboratory: Sarah Akers, Marjolein T. Oostrom and Steven R. Spurgeon

The authors would like to thank Drs. Jenna Pope, Elizabeth Kautz, and Matthew Olszta for reviewing the code and related manuscript. This research was supported by the I3T Commercialization Laboratory Directed Research and Development (LDRD) program at Pacific Northwest National Laboratory (PNNL). PNNL is a multiprogram national laboratory operated for the U.S. Department of Energy (DOE) by Battelle Memorial Institute under Contract No. DE-AC05-76RL0-1830. Experimental sample preparation was performed at the Environmental Molecular Sciences Laboratory (EMSL), a national scientific user facility sponsored by the Department of Energy's Office of Biological and Environmental Research and located at PNNL. TEM data was collected in the Radiological Microscopy Suite (RMS), located in the Radiochemical Processing Laboratory (RPL) at PNNL. C.D.,S.G., W.C., W.Q. and S.B. acknowledge support from the University of Washington Clean Energy Institute and the National Science Foundation Research Traineeship under Award NSF DGE-1633216.

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