| --- |
| title: MyoSeg |
| emoji: 🔬 |
| colorFrom: red |
| colorTo: red |
| sdk: streamlit |
| sdk_version: "1.45.1" |
| app_file: src/streamlit_app.py |
| pinned: true |
| license: mit |
| --- |
| |
| # MyoSeg — Myotube & Nuclei Analyzer |
|
|
| **Version v12 (Oct 2026).** Default preset *Tuned to manual counts*: calibrated against 55 manually |
| counted C2C12 wells. Held-out error: fusion index ±4.9 points, MyHC⁺ % ±4.9, myotube count r = 0.80 |
| (see `tuning/VALIDATION.md`). *Training defaults* reproduces the earlier v9 results. |
|
|
| Deep-learning quantification of myotubes and nuclei in C2C12 immunofluorescence |
| images (red = MyHC, blue = DAPI): nuclei, myotubes, MyHC⁺ nuclei, fusion index |
| (cytoplasm-hole or pixel-overlap), nuclei per myotube and myotube area. |
|
|
| ## Code layout |
|
|
| | File | What it does | |
| |---|---| |
| | `src/myoseg_core.py` | The analysis engine — model, inference, postprocessing, metrics, quality checks, diagnosis. No Streamlit. | |
| | `src/streamlit_app.py` | The web app (UI only; calls the core). | |
| | `analyze_folder.py` | Command-line batch analysis using the same core. | |
| | `mcp_server.py` | MyoSeg as a tool for AI assistants (Model Context Protocol). | |
| | `benchmark.py` | Accuracy vs hand-labelled masks (Dice, count/FI error, uncertainty vs error). | |
| | `self_train.py` | Safe continual-learning / retraining pipeline. | |
| | `tests/` | v9 equivalence test, MCP end-to-end test, synthetic image generator. | |
|
|
| ## Using the core from Python |
|
|
| ```python |
| import sys; sys.path.insert(0, "src") |
| import myoseg_core as core |
| |
| model, info = core.load_model("model_final.pt") |
| params = core.make_params("Dense culture rescue", px_um=0.65) |
| res = core.analyze_image(model, core.load_rgb("img.tif"), params) |
| print(core.public_metrics(res["metrics"])) |
| print(core.compare_presets(res["probs"], res["myc_full"], params)) # no extra inference |
| print(core.quality_check(core.load_rgb("img.tif"), params, res["probs"])) |
| print(core.diagnose(res["metrics"], params)) |
| ``` |
|
|
| ## Use MyoSeg from Claude (MCP server) |
|
|
| MyoSeg can be called directly by Claude Desktop, Claude Code or any MCP client. |
| Images stay on your computer and no API key is needed. |
|
|
| ```bash |
| pip install -r requirements-mcp.txt |
| python mcp_server.py # the client normally launches this for you |
| ``` |
|
|
| **Claude Desktop** — add to `claude_desktop_config.json`, then restart Claude: |
|
|
| ```json |
| {"mcpServers": {"myoseg": {"command": "python", "args": ["/ABSOLUTE/PATH/TO/myoseg/mcp_server.py"]}}} |
| ``` |
|
|
| **Claude Code:** `claude mcp add myoseg -- python /ABSOLUTE/PATH/TO/myoseg/mcp_server.py` |
|
|
| Then ask, for example: *"Analyze ~/data/day5/control and ~/data/day5/treated with MyoSeg |
| and compare the fusion index. Show me the overlay for the most uncertain image."* |
|
|
| Tools: `analyze_image`, `analyze_folder`, `compare_presets`, `compare_groups` |
| (Welch t, Mann-Whitney, Cohen's d), `quality_check`, `get_overlay`, `list_parameters`, `model_info`. |
|
|
| ## Test-time augmentation and uncertainty |
|
|
| `tta="flip4"` or `"d8"` (sidebar: *Test-time augmentation*) averages the U-Net over |
| flipped/rotated copies. Every image also gets a `model_uncertainty` score |
| (0 = sure … 1 = coin-flip); uncertain images are queued for labelling and listed |
| first in the correction panel. Set `UNCERTAINTY_FLAG_THR` from `benchmark.py` output. |
|
|
| ## Benchmark against your labels |
|
|
| ```bash |
| python benchmark.py /path/to/HELD_OUT_data --tta none flip4 d8 --sizes 512 768 |
| ``` |
| Use images the model was **not** trained on. |
|
|
