Update README.md

Add model card with usage instructions

README.md

@@ -1,3 +1,92 @@
----
-license: apache-2.0
----
+---
+library_name: pytorch
+license: apache-2.0
+tags:
+- medical-image-segmentation
+- interactive-segmentation
+- nnunet
+- meningioma
+- radiotherapy
+pipeline_tag: image-segmentation
+---
+
+# Interactive-MEN-RT
+
+**Domain-Specialized Interactive Segmentation for Meningioma Radiotherapy Planning**
+
+**Status**: Research prototype — Not for clinical use.
+
+## Overview
+
+Interactive 3D meningioma segmentation framework built on nnU-Net v2 and nnInteractive.
+
+- **Performance**: 77.6% Dice, 64.8% IoU (BraTS 2025 Meningioma RT)
+- **Interaction Modes**: Point, scribble, box, lasso
+- **Input**: T1c MRI (contrast-enhanced)
+
+## Files
+```
+nnUNetInteractionTrainer__nnUNetPlans__3d_fullres_scratch/
+├── plans.json
+├── dataset.json
+└── fold_0/
+    ├── checkpoint_best.pth (820 MB)
+    └── checkpoint_final.pth (820 MB)
+```
+
+## Quick Start
+```python
+from huggingface_hub import snapshot_download
+import os
+
+# Download checkpoint
+root = snapshot_download(
+    "hanjang/Interactive-MEN-RT",
+    allow_patterns=["nnUNetInteractionTrainer__nnUNetPlans__3d_fullres_scratch/**"]
+)
+CKPT = os.path.join(root, "nnUNetInteractionTrainer__nnUNetPlans__3d_fullres_scratch")
+
+# Load and run inference
+from Interactive_MEN_RT_predictor import InteractiveMENRTPredictor
+import torch, numpy as np
+
+predictor = InteractiveMENRTPredictor(
+    device=torch.device("cuda" if torch.cuda.is_available() else "cpu")
+)
+predictor.initialize_from_trained_model_folder(
+    model_training_output_dir=CKPT,
+    use_fold=0,
+    checkpoint_name="checkpoint_best.pth"
+)
+
+# Run on your volume (shape: 1, H, W, D)
+predictor.reset_interactions()
+predictor.set_image(volume)
+predictor.set_target_buffer(np.zeros_like(volume[0], np.float32))
+predictor._finish_preprocessing_and_initialize_interactions()
+predictor._predict_without_interaction()
+prediction = (predictor.target_buffer > 0.5).astype(np.uint8)
+```
+
+# Citation
+
+```bibtex
+@inproceedings{interactive-men-rt-2025,
+  title={Domain-Specialized Interactive Segmentation Framework for Meningioma Radiotherapy Planning},
+  author={Lee, Junhyeok and Jang, Han and Choi, Kyu Sung},
+  booktitle={MICCAI CLIP Workshop},
+  year={2025}
+}
+```
+
+# Links
+
+GitHub: [snuh-rad-aicon/Interactive-MEN-RT](https://github.com/snuh-rad-aicon/Interactive-MEN-RT)
+
+Contact: janghan001112@gmail.com
+
+Developed at Seoul National University AICON Lab
+
+Research only. Not for clinical use.
+
+---