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3D-SkipDenseSeg

Skip-connected 3D DenseNet for volumetric infant brain MRI segmentation

By Toan Duc Bui, Jitae Shin, Taesup Moon

This is the implementation of our method in the MICCAI Grand Challenge on 6-month infant brain MRI segmentation-in conjunction with MICCAI 2017 in Pytorch.

Introduction

6-month infant brain MRI segmentation aims to segment the brain into: White matter, Gray matter, and Cerebrospinal fluid. It is a difficult task due to larger overlapping between tissues, low contrast intensity. We treat the problem by using very deep 3D convolution neural network. Our result achieved the top performance in 6 performance metrics.

Citation

@article{bui2019skip,
  title={Skip-connected 3D DenseNet for volumetric infant brain MRI segmentation},
  author={Bui, Toan Duc and Shin, Jitae and Moon, Taesup},
  journal={Biomedical Signal Processing and Control},
  volume={54},
  pages={101613},
  year={2019},
  publisher={Elsevier}
}

Requirements:

• Pytorch >=0.4, python 3.0, Ubuntu 14.04
• TiTan X Pascal 12GB

Installation

• Step 1: Download the source code

https://github.com/tbuikr/3D-SkipDenseSeg.git
cd 3D-SkipDenseSeg

• Step 2: Download dataset at http://iseg2017.web.unc.edu/download/ and change the path of the dataset data_path and saved path target_path in file prepare_hdf5_cutedge.py

data_path = '/path/to/your/dataset/'
target_path = '/path/to/your/save/hdf5 folder/'

• Step 3: Generate hdf5 dataset

python prepare_hdf5_cutedge.py

• Step 4: Run training

python train_v2.py

Run evaluation result.

python val.py

We also provide pretrained model. Use the pretrained model, you should achieve the result as the table.

Dice Coefficient (DC) for 9th subject (9 subjects for training and 1 subject for validation)

Run on testing set

python test.py