# Liver Tumors Segmentation-CNNs

This project segments tumors in the Liver using 2 cascaded CNNs. We use [3D-IRCADb 01](https://www.ircad.fr/research/3d-ircadb-01/) as our dataset.

View the [Thesis](https://drive.google.com/file/d/1UpkakPGMc2Cvtik6IEs9TqxjxvW4n9Up/view?usp=sharing) 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 :grinning: )

```

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

```