---
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.