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Medical Image Segmentation with U-Net

Python code for medical image segmentation using U-Net architecture. This repository is organized into three modules, each serving a specific purpose in the segmentation process.

Modules

Module 1: Data Preprocessing and Slicing

  • Load medical image volumes and masks (NIfTI format).
  • Normalize image intensity and slice volumes.
  • Save 2D slices as PNG images.

Module 2: U-Net Model Training

  • Implement U-Net for semantic segmentation.
  • TensorFlow and Keras used for training.
  • Save model checkpoints and allow continuation.
  • Visualize training data with niwidgets.

Module 3: Image Segmentation and 3D Mesh Generation

  • Load trained U-Net model for predictions.
  • Visualize original, scaled, and predicted slices.
  • Generate 3D mesh with marching cubes algorithm.
  • Save resulting mesh as STL file.

Usage

  1. Install dependencies: pip install -r requirements.txt
  2. Execute modules based on your requirements.

Dependencies

  • nibabel
  • numpy
  • matplotlib
  • opencv-python
  • tensorflow
  • keras
  • niwidgets
  • scikit-image
  • meshplot
  • numpy-stl

Getting Started

  1. Clone the repository:

    git clone https://github.com/chiragJoshi24/NIfTI-to-3D-Model-Converter.git
    cd NIfTI-to-3D-Model-Converter
  2. Install dependencies:

    pip install -r requirements.txt

Dataset for Model Training

To train the model, you can download the dataset from Cancer Imaging Archive.

Contribution

Contributions are welcome! Feel free to report issues or suggest improvements.

nifti-to-3d's People

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