MeshNet

This repository contains a PyTorch implementation of MeshNet architecture. MeshNet is a volumetric convolutional neural network for image segmentation (focused on brain imaging application) based on dilated kernels [1].

This code provides a framework for training and evaluating a model for segmentation of a T1 (+ optional T2) into a 104 class brain atlas. It is a modification of our previous work [3].

Usage

Data preparation

1. Prepare T1 or T2 input with mri_convert from FreeSurfer (https://surfer.nmr.mgh.harvard.edu/) conform T1 to 1mm voxel size in coronal slice direction with side length 256. You can skip this step if your T1 image is already with slice thickness 1mm x 1mm x 1mm and 256 x 256 x 256.

mri_convert [brainDir]/t1.nii [brainDir]/T1.nii.gz -c

2. Prepare labels from aparc+aseg.nii.gz using:

python prepare_data.py --brains_list [brains_lits.txt]

Training

To train the model use the following command:

python main.py --model ./models_configurations/MeshNet_104_38.yml --train_path ./folds/hcp_example/train.txt --validation_path ./folds/hcp_example/validation.txt

We also support Visdom (https://github.com/facebookresearch/visdom) monitoring during training. To use it use arguments:

--visdom --visdom_server [visdom_server_ip] --visdom_port [visdom_server_port]

Evaluation

To evaluate the model use the following command:

python evaluation.py --models_file [models_list.txt] --evaluation_path folds/hcp_example/test.txt

Requirements

• Install PyTorch https://pytorch.org/get-started/locally/
• Install other dependencies

pip install -r requirements.txt

Acknowledgment

This work is supported by grants IIS-1318759, R01EB020407, R01EB006841, P20GM103472, P30GM122734.

Data were provided [in part] by the Human Connectome Project, WU-Minn Consortium (Principal Investigators: David Van Essen and Kamil Ugurbil; 1U54MH091657) funded by the 16 NIH Institutes and Centers that support the NIH Blueprint for Neuroscience Research; and by the McDonnell Center for Systems Neuroscience at Washington University.

References

[1] https://arxiv.org/abs/1511.07122 Multi-Scale Context Aggregation by Dilated Convolutions. Fisher Yu, Vladlen Koltun
[2] https://arxiv.org/abs/1612.00940 End-to-end learning of brain tissue segmentation from imperfect labeling. Alex Fedorov, Jeremy Johnson, Eswar Damaraju, Alexei Ozerin, Vince D. Calhoun, Sergey M. Plis
[3] https://arxiv.org/abs/1711.00457 Almost instant brain atlas segmentation for large-scale studies. Alex Fedorov, Eswar Damaraju, Vince Calhoun, Sergey Plis
[4] http://www.humanconnectomeproject.org/ Human Connectome Project

Previously

Brain Atlas segmentation with brainchop.org

To get brain atlas segmentation ([3]) you don't need to run any code. Just sign up at brainchop.org, upload your 3T MRI T1 image and get brain atlas in 1-2 minutes.

Watch video with example of brain atlas segmentation.

Torch Implementation for brain tissue segmentation https://github.com/Entodi/MeshNet

Result on subject 105216

T1 MRI	FreeSurfer	MeshNet

Questions

If you have any questions about implementation and training, don't hesitate to either open an issue here or send an email to eidos92@gmail.com.