This project demonstrates the use of a pre-trained U-Net model for medical image segmentation. U-Net is a powerful convolutional neural network architecture specifically designed for biomedical image processing tasks. This example showcases how to load a trained model and perform inference on sample test images.
- Model: U-Net (pre-trained)
- Framework: PyTorch
- Task: Semantic segmentation of medical images
- Application: Highlighting anatomical structures in biomedical images
- Hardware: CPU (for demonstration purposes)
- Load and use a pre-trained U-Net model for inference
- Segment medical images with high localization accuracy
- Visualize input images and their corresponding segmented outputs
- Easy to run on Google Colab (no GPU required)
Medical_Image_Segmentation.py– Python file with step-by-step implementationREADME.md– Project overview and instructions
- Open the notebook in Google Colab.
- Run each cell sequentially to:
- Install dependencies
- Load the pre-trained U-Net model
- Perform segmentation on sample medical images
- Visualize results
- numpy==1.23.5
- pandas==2.2.2
- matplotlib==3.9.0
- opencv-python-headless==4.10.0.82
- torch==2.3.1
- torchvision==0.18.1
- scikit-image==0.24.0
- nibabel==5.2.1
- scikit-learn
The notebook loads a pre-trained U-Net model from remote storage and applies it to segment sample biomedical images. The U-Net model’s architecture is based on an encoder-decoder structure with skip connections, enabling precise localization even with limited data. Since the demo runs on a CPU, it emphasizes model behavior and output over performance.
The model successfully segments anatomical features from test images, demonstrating its effectiveness in biomedical image analysis tasks.
- U-Net architecture: Olaf Ronneberger et al., "U-Net: Convolutional Networks for Biomedical Image Segmentation"
- CognitiveClass.ai
This project is for educational and demonstration purposes.