Tutorial: Measure particle size distribution via SEM

This tutorial explains how to collect particle-size statistics with AutoEMX using the script:

autoemx/scripts/Particle size Statistics measurements/Collect_Particle_Statistics.py

It also explains how to post-process results and, for advanced users, how to add a separate particle-segmentation model.

What this script does

Collect_Particle_Statistics.py:

  • Acquires SEM frames across the selected sample region.

  • Segments particles in each frame.

  • Filters particles by size.

  • Continues until the requested number of particles is reached (or frames are exhausted).

  • Saves per-particle measurements and summary statistics.

  • Saves a particle-size histogram.

Step 1 - Open script to edit

Open:

autoemx/scripts/Particle size Statistics measurements/Collect_Particle_Statistics.py

When you run it, a GUI appears to collect the main inputs.

Step 2 - Define required inputs in the GUI

Fill in the following fields:

  • Sample ID

    Name used to create the output folder and output files.

  • Sample Center X, Y

    Stage coordinates (mm) of the center of the sample.

  • Number of Particles to Analyse

    Target number of particles to analyse.

  • Min & Max Particle Diameter (um)

    Accepted particle-diameter range (used to compute area filters). It is recommended to split measurements into multiple runs, each covering at most one order of magnitude (for example, 1 um to 10 um). This is because frame dimensions are chosen based on the largest accepted particle diameter; if the range is too large, the pixel size can become comparable to the smallest particles, which can invalidate their area measurements.

  • Carbon Tape Diameter (mm)

    Used to estimate the effective sample area to scan over the carbon tape.

  • Working Distance (mm)

    Nominal working distance used by autofocus to prevent catastrophic focus drift.

  • Segmentation Model

    Particle segmentation model. Default is threshold_bright. This default model works best when there is clear contrast between carbon tape and particles. For this reason, electron backscatter detector mode is recommended to maximize Z-contrast. Particles should also be well separated; touching/overlapping particles can be segmented as a single particle. For more complex samples, machine-learning segmentation models are recommended (for example Rettenberger2024). You can also add your own segmentation model (see the advanced section below).

  • Manual Navigation

    If enabled, you manually navigate to regions/particles and determine the frame size.

  • Auto Detect Carbon Tape

    Enables automatic substrate detection for carbon tape workflows.

  • Auto Adjust Brightness and Contrast

    If set to No, you must also provide:

    • Brightness

    • Contrast

After submitting, choose results_dir (save directory) in the folder picker.

Step 3 - Run the script

Note that the measurement runs using the microscope’s current settings, including detector type, beam current, and acceleration voltage.

Run from your AutoEMX environment, for example:

python "autoemx/scripts/Particle size Statistics measurements/Collect_Particle_Statistics.py"

Output

In the sample output directory, the workflow writes:

  • <SampleID>_Par_sizes.csv

    Per-particle table with particle ID, frame ID, area, and equivalent diameter.

  • <SampleID>_Par_size_stats.csv

    Summary statistics (mean, stdev, median, min, max, D10, D25, D75, D90).

  • <SampleID>_Par_size_distribution_hist.png

    Histogram plot of equivalent particle diameters.

  • SEM images collected during the scan (depending on script options).

Optional: Reprocess particle statistics after filtering

If some particles or full frames were incorrectly segmented and you want to exclude them before recomputing the size distribution, use:

autoemx/scripts/Particle size Statistics measurements/Process_Particle_Stats_Files.py

Main editable fields in that script are:

  • sample_ID: sample folder to process

  • input_dir: parent path where sample folder is stored

  • particles_IDs_to_filter: list of particle IDs to ignore

  • frame_IDs_to_filter: list of frame IDs to ignore (if a full frame is bad, all its particles are ignored)

The script generates processed outputs with suffix _processed.

Optional (Advanced): Add a separate segmentation model

Most users do not need this section. Use it only if the built-in segmentation models are not suitable for your images.

AutoEMX automatically discovers custom segmentation models from:

autoemx/core/em_runtime/particle_segmentation_models/

To add a new model:

  1. Create a new Python file in that folder, for example:

    my_segmentation_model.py

  2. Use

    autoemx/core/em_runtime/particle_segmentation_models/segmentation_model_template.py as the template.

  3. Implement a function with this signature:

def segment_particles(frame_image, powder_meas_config=None, save_image=False, EM=None):
                ...
                return par_mask

  1. Return either:

    • a binary mask (background 0, particles > 0), or

    • a labeled image (background 0, each particle with a unique positive label).

  2. Restart the particle-statistics script. Your model name should appear in the

    Segmentation Model dropdown automatically.

  3. Select it in the GUI (or set par_segmentation_model directly in

    powder_meas_cfg_kwargs).

Notes:

  • If your model requires external files (for example ONNX weights), keep paths

    stable and preferably relative to the model file location.

  • If a model name is invalid, AutoEMX falls back to threshold_bright.