liver-tumor-segmentation-using-cnns's Introduction

Liver Tumors Segmentation-CNNs

This project segments tumors in the Liver using 2 cascaded CNNs. We use 3D-IRCADb 01 as our dataset. View the Thesis for more details about the model, dataset, methodology and results.

There are 3 notebooks in this project

3d-ircadb-01_util.ipynb Responsible for renaming files, merging masks and augmenting the dataset
liver_CNN.ipynb The first CNN, it uses a CNN to segment the Liver and extract the ROI
tumor_CNN_final.ipynb The second CNN, it segments the tumors using a CNN after masking other organs using the extracted ROI from the first CNN

There is the models directories which contains the trained models

liver_model_final_resunet.h5 which is the first CNN trained for 20 epochs
tumor_weights_final_50epochs.h5 which is the second CNN trained for 50 epochs
tumor_weights_final_100epochs.h5 which is the second CNN trained for 100 epochs

Dependencies

I used Anaconda for package management but pip will work all the same

Tensorflow-GPU
Keras (Tensorflow) already comes with Tensorflow
Numpy
OpenCV
Matplotlib
Pydicom
Jupyter Notebook
Scipy
Scikit-learn
PIL
Seaborn
Imageio

Dataset Structure

The structure of the dataset should be as follows ( if you don't want to change the code 😀 )

train
|_ patients               # Contains all the CT-slices from all patients together with no directories inside
|_ masks                  # Contains the masks for the Liver and Tumors for the patients
   |_ merged_livertumors  # Contains the masks of tumors after merging each slice's tumor masks together
   |_ 1.1_liver           # Contains the mask for the liver of each slice for patient 1
   |_ 1.2_liver           # Contains the mask for the liver of each slice for patient 2
   |_ 1.3_liver           # Contains the mask for the liver of each slice for patient 3
      .
      .
      .
   |_ 1.20_liver          # Contains the mask for the liver of each slice for patient 20

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