cwlachap/hrnet-cephalometric-landmark-detection

HRNet Cephalometric Landmark Detection

This model performs automatic detection of 19 anatomical landmarks in lateral cephalometric radiographs using HRNet-W32 architecture.

🦷 Model Description

• —Architecture: HRNet-W32 (High-Resolution Network)
• —Task: 19-point cephalometric landmark detection
• —Dataset: ISBI Lateral Cephalograms
• —Input Size: 768×768 pixels
• —Output: 19 landmark coordinates (x, y)
• —Model Size: 331.1 MB

📍 Landmarks Detected

1. 1.Sella turcica - Center of pituitary fossa
2. 2.Nasion - Frontonasal suture
3. 3.Orbitale - Lowest point of orbital cavity
4. 4.Porion - Highest point of acoustic meatus
5. 5.Subspinale (Point A) - Deepest midline point on maxilla
6. 6.Supramentale (Point B) - Deepest midline point on mandible
7. 7.Pogonion - Most prominent midline point of chin
8. 8.Menton - Lowest point of mandibular symphysis
9. 9.Gnathion - Midpoint between Pogonion and Menton
10. 10.Gonion - Corner of the jaw angle
11. 11.Lower Incisor Tip - Tip of lower central incisor
12. 12.Upper Incisor Tip - Tip of upper central incisor
13. 13.Upper Lip - Most prominent point of upper lip
14. 14.Lower Lip - Most prominent point of lower lip
15. 15.Subnasale - Junction between nose and upper lip
16. 16.Soft Tissue Pogonion - Most prominent point of chin in profile
17. 17.Posterior Nasal Spine - Tip of posterior nasal spine
18. 18.Anterior Nasal Spine - Tip of anterior nasal spine
19. 19.Articulare - Junction of temporal bone and mandible

🚀 Usage

Quick Start with Streamlit

import streamlit as st
import torch
from huggingface_hub import hf_hub_download

# Download model
@st.cache_resource
def load_model():
    model_path = hf_hub_download(
        repo_id="cwlachap/hrnet-cephalometric-landmark-detection",
        filename="best_model.pth"
    )
    
    # Load your HRNet model here
    model = get_hrnet_w32(config)
    checkpoint = torch.load(model_path, map_location='cpu')
    model.load_state_dict(checkpoint['model_state_dict'])
    model.eval()
    return model

model = load_model()

Python API

import torch
from huggingface_hub import hf_hub_download

# Download model
model_path = hf_hub_download(
    repo_id="cwlachap/hrnet-cephalometric-landmark-detection",
    filename="best_model.pth"
)

# Load model
model = get_hrnet_w32(config)
checkpoint = torch.load(model_path, map_location='cpu')
model.load_state_dict(checkpoint['model_state_dict'])
model.eval()

# Perform inference
with torch.no_grad():
    landmarks = model(input_image)

📊 Performance

• —Mean Radial Error (MRE): ~1.2-1.6mm
• —Successful Detection Rate (SDR@2mm): ~80-85%
• —Successful Detection Rate (SDR@2.5mm): ~88-92%
• —Training Time: ~15-20 hours on RTX 4070 Ti SUPER

🏥 Applications

• —Orthodontic Treatment Planning: Automated cephalometric analysis
• —Research: Large-scale cephalometric studies
• —Education: Teaching cephalometric landmark identification
• —Clinical Decision Support: Assisting radiological assessment

⚠️ Limitations

• —Designed for lateral cephalometric radiographs only
• —Performance may vary on images with different acquisition parameters
• —Intended for research and educational purposes
• —Clinical use requires validation by qualified professionals

📝 Citation

If you use this model in your research, please cite:

@misc{hrnet-cephalometric-2024,
  title={HRNet for Cephalometric Landmark Detection},
  author={cwlachap},
  year={2024},
  url={https://huggingface.co/cwlachap/hrnet-cephalometric-landmark-detection}
}

📄 License

This model is released under the MIT License, making it free for both academic and commercial use.

🤝 Contributing

This is an open-source project! Contributions, issues, and feature requests are welcome.

• —Repository: [GitHub Repository URL]
• —Issues: [GitHub Issues URL]
• —Discussions: Use the Community tab above

🙏 Acknowledgments

• —ISBI Challenge for providing the cephalometric dataset
• —HRNet authors for the excellent architecture
• —The medical imaging community for advancing automated analysis techniques

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