Materials Data Science and Informatics (IAS-9) · Forschungszentrum Jülich
Automated segmentation and tracking of grain growth in TEM data
The in situ TEM videos contain hundreds to thousands of frames, making manual annotation and identity tracking slow and impractical. I am developing a pipeline that moves from single-frame segmentation to persistent grain identities so researchers can quantify area, morphology, boundary motion, and grain growth over time.
My contributionDesign and implementation of the segmentation-to-tracking workflow, from grain-mask generation to persistent identity assignment across TEM frames.
Grain segmentation
Convert difficult and changing microscopy contrast into grain-level masks that establish the objects to be tracked.
- In situ TEM
- Instance segmentation
- SAM · SAM2 · SAM3
- Prompt-based segmentation
- MatSAM
- U-Net
- EBSD data exploration
Video object tracking
Preserve each grain’s identity through boundary motion, growth, shrinkage, and changing neighbours, so its evolution can be quantified.
- Video object tracking
- Persistent IDs
- Hungarian algorithm
- SAM2 · SAM3 video segmentation
- Prompt-based segmentation
- PyTorch · OpenCV
