Dental Canal Cbct

Teeth canal detection from CBCT 3D images using deep learning

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 indside 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 'dataset' folder in project directory and follow this order:

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

Dataset class in this project provides two types of loading data into model:

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

Usage

• Dataset
  Once your dataset is in this 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

• Configs
  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 "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> 

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