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SEM (Scanning Electron Microscopy) analysis automatically detects and segments objects from micrographs, then extracts morphological features for each object. The model provides per-object confidence scores, and unreliable detections are filtered out before results are presented.

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Overview

The SEM pipeline processes data in four stages:
  1. Image preparation: detects and removes the metadata banner, reads the scalebar for spatial calibration
  2. Segmentation: detects and segments individual objects using a deep learning model with uncertainty estimation
  3. Feature extraction: measures morphological properties per object, including size, shape, and spacing metrics
  4. Filtering: removes low-confidence detections and flags objects near the image border

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Key Metrics

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Adding Data

Upload SEM images (PNG or TIF) through the data page. Analysis begins automatically once the upload completes.

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Viewing Results

Once processing completes, the workspace shows several sections:Image viewer: Displays the SEM image with a segmentation fingerprint overlay highlighting detected objects.Outlier detection: An interactive feature analysis view. Use the facet dropdown to switch between groups of related features (size, shape, spacing, etc.). Each feature is displayed as a histogram with objects classified as inliers or outliers:
  • Green: outlier (low end)
  • Blue: inlier
  • Red: outlier (high end)
Drag the boundary lines on any histogram or enter numeric values to adjust the outlier thresholds. Bounds are saved per browser automatically.

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Configuration

Open the configuration drawer (gear icon) to set spatial calibration. Enter the pixel distance and corresponding real-world distance with units to convert measurements from pixels to physical units. You can save and load workflow configuration presets from the dropdown at the top of the drawer.