C-DenseUNet: 2D-3D Coupled Densely Connected UNet for Liver and Tumor Segmentation from CT Volumes.

by Zengming Shen,Bogdan Georgescu, Thomas S. Huang.

Introduction

This repository is for the source code of C-DenseUNet: 2D-3D Coupled Densely Connected UNet for Liver and Tumor Segmentation from CT Volumes.

Usage

1. Data preprocessing: Download dataset from: Liver Tumor Segmentation Challenge.
   Then put 131 training data with segmentation masks under "data/TrainingData/" and 70 test data under "data/TestData/".
   Run:

   python preprocessing.py 

2. Test our model: Download liver mask from LiverMask and put them in the folder: 'livermask'.
   Download model from Model and put them in the folder: 'model'. run:

   python test.py

3. Train 2D DenseUnet: First, you need to download the pretrained model from ImageNet Pretrained, extract it and put it in the folder 'model'. Then run:

   sh bash_train.sh

4. Train C-DenseUnet: Load your trained model and run

   CUDA_VISIBLE_DEVICES='0' python train_hybrid.py -arch 3dpart

5. Train C-DenseUnet in end-to-end way:

   CUDA_VISIBLE_DEVICES='0' python train_hybrid.py -arch end2end

Questions

Please contact '[email protected]'