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Optical image analysis automatically segments features from sequences of optical microscopy images 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 sequence.

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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

Optical image data is a sequence of still images (.png, .jpg, .tif, or .bmp), with each image treated as one frame. Analysis runs incrementally: each time new images arrive for a data item, the new frames are analyzed and appended to the existing results.

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Viewing Results

Once processing completes, the workspace shows several sections:Video player: Scrub through the image sequence frame by frame. The images are assembled into a video for playback, and 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 frames. After the first 32 frames, each metric also gets three anomaly-scoring series that flag frames with unusual shape changes: a z-score, an EMA z-score, and an anomaly probability. These appear as separate series, for example Perimeter Px Z Score, Perimeter Px Ema Z Score, and Perimeter Px Anomaly Probability.A status badge indicates pipeline progress. The refresh button (Refresh content) reloads the latest results but doesn’t re-run analysis. To re-run analysis, use Actions > Reanalyze.