Link model to paper and official code repository

This PR improves the model card for LegSegNet by:
- Linking the model to its research paper: [LegSegNet: A Public Deep Learning System for Lower Extremity CT Tissue Segmentation and Quantification](https://huggingface.co/papers/2605.30829).
- Updating the GitHub repository link to the official lab version (`mazurowski-lab/LegSegNet`).
- Adding detailed label information based on the `dataset.json` configuration.
- Including usage instructions and a citation section.

README.md

@@ -1,42 +1,73 @@
 ---
-license: cc-by-nc-4.0
 library_name: pytorch
-tags:
-  - medical-image-segmentation
-  - ct
-  - lower-extremity
-  - body-composition
-  - computer-vision
+license: cc-by-nc-4.0
 pipeline_tag: image-segmentation
+tags:
+- medical-image-segmentation
+- ct
+- lower-extremity
+- body-composition
+- computer-vision
 ---
 
 # LegSegNet
 
-**GitHub:** [https://github.com/gogochen07/LegSegNet](https://github.com/gogochen07/LegSegNet)
+LegSegNet is a deep learning system for lower extremity CT tissue segmentation and body composition quantification, introduced in the paper [LegSegNet: A Public Deep Learning System for Lower Extremity CT Tissue Segmentation and Quantification](https://huggingface.co/papers/2605.30829).
 
-**LegSegNet** is a deep learning system for lower extremity CT tissue segmentation and body composition quantification. 
+**GitHub:** [https://github.com/mazurowski-lab/LegSegNet](https://github.com/mazurowski-lab/LegSegNet)
 
-Given an input CT scan, LegSegNet segments four tissue compartments: **bone**, **skeletal muscle**, **subcutaneous adipose tissue (SAT)**, and **inter- and intramuscular adipose tissue (IMAT)**.
+Given an input CT scan, LegSegNet segments four tissue compartments: **bone**, **skeletal muscle**, **subcutaneous adipose tissue (SAT)**, and **inter- and intramuscular adipose tissue (IMAT)**. 
 
 The system can further convert predicted masks into quantitative measurements, including tissue area, tissue volume, CT attenuation, and tissue-volume ratios, supporting downstream medical image analysis.
 
 ## Model Details
 
 - **Task:** Lower extremity CT tissue segmentation
-- **Input:** Lower extremity CT images/volumes
+- **Input:** Lower extremity CT images (PNG) or volumes (NIfTI)
 - **Output:** Multi-class segmentation mask and body composition measurements
-- **Labels:** Background, SAT, skeletal muscle, IAT, bone
+- **Labels:** 
+  - 0: Background
+  - 1: SAT (Subcutaneous adipose tissue)
+  - 2: Muscle (Skeletal muscle)
+  - 3: Inter/Intra (Inter- and intramuscular adipose tissue)
+  - 4: Bone
 
 ## Usage
 
-Please refer to the GitHub repository for more details:
+LegSegNet uses a pretrained nnU-Net model. For a practical end-to-end workflow including a Gradio interface, please refer to the official GitHub repository.
+
+### Installation and Inference
+
+To run the application locally:
+
+```bash
+pip install -r requirements.txt
+python app.py
+```
+
+### File Structure
 
-[https://github.com/gogochen07/LegSegNet](https://github.com/gogochen07/LegSegNet)
+Ensure the model files are organized as follows to use the inference scripts:
+```text
+model/
+|-- plans.json
+|-- dataset.json
+|-- fold_0/
+    |-- checkpoint_best.pth
+```
 
 ## Citation
 
-Please cite the following manuscript if you find the model useful:
+If you find LegSegNet useful, please cite the following manuscript:
 
+```bibtex
+@article{legsegnet2026,
+  title={LegSegNet: A Public Deep Learning System for Lower Extremity CT Tissue Segmentation and Quantification},
+  author={Gao, Chen and others},
+  journal={arXiv preprint arXiv:2605.30829},
+  year={2026}
+}
+```
 
 ## License