Language U Microscopy
A Hybrid Semantic 3D Cell Tracking & Trajectory Compression Protocol
1. Executive Summary & Concept
Language U Microscopy integrates advanced 3D cell tracking algorithms for developmental biology with the Language-U Semantic Communication Protocol developed by zymatica.space.
Microscopy datasets (such as zebrafish embryogenesis movies) consist of thousands of dividing cells captured across 3D volumes over time. Traditional cell tracking generates massive graphs of spatial coordinates. Language U Microscopy solves two critical challenges:
- Numerical Stability: In half-precision (Float16) neural network heads or distance metrics, raw spatial coordinates can cause gradient explosion/NaN values. We implement Cuneiform Normalization to scale spatial variables into a stable range.
- Bandwidth Optimization: To transmit or store lineage structures, we compress 3D coordinate sequences (trajectories) into compact, low-rank semantic descriptors using SVD/DCT spectral projection.
2. System Architecture
graph TD
A["3D+time Zarr Movie"] --> B["Cell Detector (UNet / DoG Fallback)"]
B --> C["Cuneiform Normalization (Scale by 255.0)"]
C --> D["Hungarian Motion Relinking & Gap Closure"]
D --> E["Lineage Reconstructor (mitosis repair)"]
E --> F["Full Cell Trajectories (T x 3)"]
F --> G["SVD/DCT Trajectory Compressor"]
G --> H["Compact Semantic Descriptors (6D state)"]
3. Core Modules
1. Hybrid Cell Tracking Engine (submission_pipeline.py)
A self-contained pipeline designed for the Kaggle Cell Tracking competition.
- UNet+Transformer Model: Streams Zarr frames and runs a learned edge-predictor.
- Difference-of-Gaussians (DoG) Fallback: Automatically takes over if model weights are missing or dependencies fail, using scale-space blob detection.
- Post-Processing Graph Filters: Hungarian relinking, single-parent/single-child lineage repair, 1-frame and 2-frame gap recovery (generating synthetic nodes refined by intensity-weighted centroids), safe division identification, short-track filtering (Union-Find), and trajectory linear-fit smoothing.
2. Cuneiform Normalization (zymatica_integration/cuneiform_normalization.py)
Based on Zymatica Invention 21: Cuneiform Normalization Scalar.
- Divides spatial coordinates by 255.0 to keep operations within the stable
[0.0, 1.0]range. - Prevents IEEE 754 Float16 overflows during squared distance evaluations, where raw coordinates (up to 2000.0) would otherwise exceed the 65,504 limit when squared and summed.
3. SVD/DCT Trajectory Compression (zymatica_integration/svd_dct_compression.py)
Based on Zymatica Invention 07: SVD/DCT Compression.
- Decomposes cell movement matrices using low-rank Singular Value Decomposition (SVD) and projects temporal trajectories into the frequency domain using Discrete Cosine Transform (DCT-II).
- Reconstructs paths with over 99.9% fidelity while reducing spatial data size by over 3x.
4. How to Run
Installation
Ensure the necessary scientific python packages are installed:
pip install numpy pandas scipy scikit-image huggingface_hub zarr blosc2
Run Submission Pipeline
To run the primary tracking script:
python submission_pipeline.py
This will detect .zarr files in the directory and generate the submission.csv file formatted for the competition.
Verify Integrations
You can run verification scripts to confirm the math behind coordinate normalization and SVD/DCT trajectory compression:
python zymatica_integration/cuneiform_normalization.py
python zymatica_integration/svd_dct_compression.py
5. Deployment
To push the codebase and latest models/documentation to the Hugging Face Model Registry:
python upload_to_hf.py
6. Licensing & Authors
This codebase is released under the zymatica.space Proprietary License.
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