Fully Convolutional Networks for Liver Segmentation

In this project, we experiment with training FCN models on a liver segmentation dataset provided by IRCAD

Set up

First, we need to set up a virtual Python 3.6 environment as follows

pip install virtualenv
virtualenv -p python3 .env
source .env/bin/activate
pip install -r requirements.txt

Download data

Then, download the dataset 3D-ircadb-01 by running the script below

sh scripts/download.sh

How to run

Preprocessing

The image format in the dataset is in DICOM format so we have to do some preprocessing stuff before converting data to TFRecord format for later training in TensorFlow.

In the scope of this project, we only segment 4 classes including liver, bone, kidney and others. Therefore, each preprocessed data sample will be a pair of

• an image - an input image of shape (512, 512) and
• a mask - a 2D array has the same spatial shape as the image indicating which class each pixel belongs to.

python run.py --mode preprocess

We use TensorFlow iterator to iterate over the dataset. To check image and mask shape, run the command below

python run.py --mode iter

Training

To train FCN models with default config, run

python run.py --mode train

Prediction

To predict output on the validation set, run

python run.py --mode predict

TensorBoard

No augmentation

With augmentation

TODOs

• Preprocessing
• Building model
• Training pipeline
• Augmentation
• Visualization
• TensorBoard

References

1. Long et al. , Fully Convolutional Networks for Semantic Segmentation. (arxiv)