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README.md

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 ---
+language: en
 license: mit
+tags:
+  - medical-imaging
+  - segmentation
+  - in-context-learning
+  - interactive-segmentation
+  - tensorflow
+  - keras
+  - ct
+  - mri
+  - onnx
+library_name: tensorflow
+pipeline_tag: image-segmentation
 ---
+
+# LISP-Net — Lightweight In-Context Slice Propagator Network
+
+LISP-Net is a lightweight, purely convolutional framework for interactive volumetric medical image segmentation. Instead of sparse clicks, it uses a single dense 2D prompt — a reference image paired with a full mask — to derive structural guidance directly from the individual patient.
+
+## Model Details
+
+- **Architecture:** Asymmetrical dual-encoder U-Net with multi-resolution SE channel-attention
+- **Parameters:** ~28M
+- **Input:** Query image (128×128) + Prompt (reference image + binary mask, stacked as 2 channels)
+- **Output:** Binary segmentation probability map (128×128)
+- **Training data:** 208 patients across 7 datasets (NAKO, TotalSegmentator, MSD, BraTS-GLI, BraTS-MEN-RT, TopCoW MR, TopCoW CT)
+
+## Performance
+
+| Benchmark | vs. | Result |
+|-----------|-----|--------|
+| 2D (offset ±5) | UniverSeg | 0.798 vs. 0.597 DSC |
+| 2D (offset ±12) | UniverSeg | 0.704 vs. 0.569 DSC |
+| 3D (SSF only) | nnInteractive | 0.665 vs. 0.705 Vol. DSC |
+| 3D (interactive) | nnInteractive | 0.879 vs. 0.810 Vol. DSC |
+
+Peak GPU memory: 164–362 MB. Per-slice latency: ~14 ms (GPU) / ~150 ms (CPU).
+
+## Usage
+
+### Python (Keras)
+
+```python
+from inference.predictor import PromptUNetPredictor
+
+# Downloads from Hugging Face automatically on first use
+predictor = PromptUNetPredictor("Machauer-P/lisp-net")
+
+mask = predictor.predict(query_image, prompt)
+```
+
+### ONNX (Browser / ONNX Runtime)
+
+Download `lisp_net_332.onnx` and use with ONNX Runtime or integrate into a web application.
+
+## Intended Use
+
+- Interactive volumetric segmentation of CT and MRI
+- Zero-shot generalization to novel anatomical targets without retraining
+- Clinical research and annotation workflows
+
+## Limitations
+
+- Requires a full 2D dense annotation as initial prompt (higher upfront effort than sparse clicks)
+- Optimized for medium-range propagation (offset ≤16 slices); distant slices may need prompt refreshes
+
+## License
+
+MIT
+
+## Citation
+
+Paper forthcoming. See [Machauer-P/lisp-net](https://github.com/Machauer-P/lisp-net) for updates.