Dental Canal Cbct

Teeth canal detection from CBCT 3D images using deep learning

Table of Contents

• 🎯 Purpose
• ✨ Features
• 🛠️ Requirements
• 📁 Dataset
• 🚀 Usage
  • Dataset Conversion
  • Configuration
  • Training
  • Prediction
  • Reconstruct 3D Volumes
• 📊 Prediction Demo
• 👋 Contributing
• 📄 License
• 🧑‍💻 Author

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🎯 Purpose

This project aims to first take raw images in DICOM (.dcm) and masks in NIFTI (.nii) format made by 3D Slicer program and create a dataloader for them. Secondly it uses these images, masks, and given ROI JSONs to train a deep learning model for segmentation and object detection.

✨ Features

• Models supported
  • UNet
• Dataset types
  • BaseDataset: Returns image, mask, bbox (bbox in format z, x, y, w, h, Z)
  • RegionalDataset: Converts your dataset to segmented ROI boxes for your model to learn segmenting inside the boxes.

🛠️ Requirements

1. Clone this repository.

   git clone https://github.com/monajemi-arman/dental-canal-cbct
   cd dental-canal-cbct/

2. Make sure you have CUDA toolkit and NVIDIA drivers installed.
3. Install the required python modules.

   pip install -r requirements.txt

📁 Dataset

Your dataset must be put in the dataset folder in the project directory and follow this structure:

dataset/
    -- first_image/
        -- roi_1_first_image.json
        -- roi_2_first_image.json
        -- ...
        -- first_image_dcm/
            -- first_image_001.dcm
            -- first_image_002.dcm
            -- ...
    -- second_image/
        -- ...

The dataset class in this project provides two types of loading data into the model:

• BaseDataset: Returns [image, mask, bounding boxes list] for every image.
• RegionalDataset: Crops image and mask based on the bounding box and returns the resulting [image, mask].

🚀 Usage

Dataset Conversion

Once your dataset is in the specified format, run the convert script to prepare numpy arrays of your images and masks along with a concise JSON of bounding boxes:

python convert.py

Configuration

All the configs related to training, dataset conversion and split, transforms, and paths are saved in config.json. You may change these parameters as you wish. Make sure to remove the all/ directory, all.json, train.json, val.json, and test.json, then re-run convert.py with the new config.

Training

python train.py

Prediction

python predict.py <path to checkpoint>

Reconstruct 3D Volumes

• [WIP]

python reconstruct_volumes.py -i all/images -m all/masks -a all.json -o all_nii

📊 Prediction Demo

We're still gathering clinical data; this prediction is when only data of 9 patients were gathered.

👋 Contributing

Contributions are welcome! If you have suggestions for improvements or new features, please open an issue or submit a pull request.

📄 License

This project is licensed under the MIT License - see the LICENSE file for details. (Note: You might need to create a LICENSE file in your project root if it doesn't exist.)

🧑‍💻 Author

• Arman Monajemi - monajemi-arman