Lisp Net

--- 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 5 datasets (NAKO, TotalSegmentator, MSD, BraTS-GLI, BraTS-MEN-RT, TopCoW MR, TopCoW CT) ## Performance | Benchmark | vs. | Result | |-----------|-----|--------| | 2D (max offset ±5) | UniverSeg | 0.798 vs. 0.597 DSC | | 2D (max offset ±12) | UniverSeg | 0.704 vs. 0.569 DSC | | 3D (SSF only) | nnInteractive | 0.660 vs. 0.680 Vol. DSC | | 3D (interactive) | nnInteractive | 0.877 vs. 0.800 Vol. DSC | Peak GPU memory: 164.4 MB (128x128 single pass), up to 394–780 MB during full 3D volumetric inference. 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.

Source context: 0 downloads · 1 likes · Pipeline image-segmentation · Library tensorflow · Repo machauer-p/lisp-net