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Optical image analysis automatically segments features from microscopy videos and tracks how their shape evolves over time. The pipeline uses a foundation segmentation model to identify regions of interest in each frame, then computes morphological metrics across the full video.

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Overview

The optical image pipeline processes data in three stages:
  1. Frame normalization: corrects for haze, uneven illumination, and low contrast
  2. Video segmentation: a deep learning model segments features frame-by-frame, maintaining temporal consistency across the video
  3. Metric computation: morphological properties are measured for each segmented feature per frame, then tracked as timeseries with statistical anomaly scoring

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Key Metrics

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Adding Data

Upload optical microscopy video files 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:Video player: Scrub through the processed video frame by frame. The player displays metadata including FPS and total frame count.Analysis region: Shows the segmentation overlay on the current frame, highlighting the regions being tracked by the pipeline.Timeseries chart: Plots morphological metrics (perimeter, circularity, edge roughness, Hausdorff distance) across all video frames. Each metric includes EMA-smoothed mean and standard deviation bands, along with anomaly scores that flag frames with unusual shape changes.A status badge indicates pipeline progress. If results need to be regenerated, use the refetch button to re-run analysis.