vascx-simplify 0.1.3

A PyTorch library for vessel and fundus image analysis (simplified rewrite)

vascx_simplify

A PyTorch library for vessel and fundus image analysis, providing GPU-accelerated preprocessing and inference utilities for medical imaging tasks.

Note: This is a simplified rewrite of rtnls_vascx_models by Eyened.

AI Usage Disclaimer

This project was developed with significant assistance from AI tools (GitHub Copilot, ChatGPT, Claude) for code organization, refactoring, documentation, and packaging.

Features

• GPU-Accelerated Preprocessing: Fast fundus image contrast enhancement with mixed precision support
• Sliding Window Inference: Efficient inference for large images
• Ensemble Models: Segmentation, classification, regression, and heatmap-based models
• HuggingFace Integration: Easy model loading from HuggingFace Hub
• Minimal Dependencies: Uses fewer dependency libraries for easier installation and maintenance

Installation

pip install vascx_simplify

From source:

git clone https://github.com/kapong/vascx_simplify.git
cd vascx_simplify
pip install -e .

Requirements

• Python >= 3.12
• PyTorch >= 1.10.0
• kornia >= 0.6.0
• scikit-learn >= 1.0.0
• scipy >= 1.7.0
• numpy >= 1.21.0
• huggingface-hub >= 0.10.0

Usage Examples

Artery/Vein Segmentation

Segment arteries (red), veins (blue), and crossings (green) from fundus images:

from vascx_simplify import EnsembleSegmentation, VASCXTransform, from_huggingface
from PIL import Image
import torch

# Load model
model_path = from_huggingface('Eyened/vascx:artery_vein/av_july24.pt')
model = EnsembleSegmentation(model_path, VASCXTransform())

# Predict
rgb_image = Image.open('fundus.jpg')
prediction = model.predict(rgb_image)  # Returns [B, H, W] with class values

Optic Disc Segmentation

Detect and segment the optic disc:

from vascx_simplify import EnsembleSegmentation, VASCXTransform, from_huggingface
from PIL import Image

model_path = from_huggingface('Eyened/vascx:disc/disc_july24.pt')
model = EnsembleSegmentation(model_path, VASCXTransform(512))

rgb_image = Image.open('fundus.jpg')
prediction = model.predict(rgb_image)  # Returns [B, H, W] with class values

Fovea Detection

Locate the fovea center using heatmap regression:

from vascx_simplify import HeatmapRegressionEnsemble, VASCXTransform, from_huggingface
from PIL import Image

model_path = from_huggingface('Eyened/vascx:fovea/fovea_july24.pt')
model = HeatmapRegressionEnsemble(model_path, VASCXTransform())

rgb_image = Image.open('fundus.jpg')
prediction = model.predict(rgb_image)  # Returns [B, M, 2] with (x, y) coordinates

fovea_x = prediction[0, 0, 0].item()
fovea_y = prediction[0, 0, 1].item()

Image Quality Assessment

Classify fundus image quality (Reject/Usable/Good):

from vascx_simplify import ClassificationEnsemble, VASCXTransform, from_huggingface
from PIL import Image

model_path = from_huggingface('Eyened/vascx:quality/quality.pt')
model = ClassificationEnsemble(model_path, VASCXTransform(have_ce=False))

rgb_image = Image.open('fundus.jpg')
prediction = model.predict(rgb_image)  # Returns [B, 3] with quality scores (already softmaxed)

# Get probabilities (already normalized)
q1_reject, q2_usable, q3_good = prediction[0].tolist()

License

MIT License - see LICENSE file for details.

Author

Phongphan Phienphanich [e-mail]

Acknowledgments

This is a simplified rewrite of rtnls_vascx_models by Eyened.