MRI Skull Stripping Using UNet3D

Project Overview

This project involves the development of a deep learning model for MRI skull stripping using the Calgary Campinas 359 dataset. The primary goal is to accurately segment brain tissue from MRI scans, which is a crucial preprocessing step for many neuroimaging studies.

Dataset

T1-weighted volumetric brain MR images used in this project is sourced from the Calgary Campinas 359 Dataset

• 359 Participants
• Acquisition matrix size 256 x 218 x [170,180]
• 1.5 T and 3 T Magnetic Field Strength
• Voxel size for images is 1 mm³

Model

The model used in this project is a 3D version of the UNet architecture, designed to handle volumetric data such as MRI scans. UNet3D is known for its encoder-decoder structure, which is particularly effective for segmentation tasks in 3D medical imaging.

Training Setup

• Optimizer: Adam
• Batch size: 64
• Epochs: 500
• Learning Rate: 0.001
• Loss Function: Cross Entropy

Requirements

To run the code in this repository, make sure you have the following dependencies installed:

• Python >=3.8
• PyTorch == 2.3 (verified working with 2.0 - 2.3, both for CPU and GPU)
• torch-summary == 1.4.5
• nibabel == 5.2.1
• monai == 1.3.2
• scikit-learn >= 0.20.1
• matplotlib >= 2.2.3

References

If you use this code or the UNet model architecture in your work, please cite the original paper of the orignal model:

O. Ronneberger, P. Fischer, and T. Brox, "U-Net: Convolutional Networks for Biomedical Image Segmentation," arXiv preprint arXiv:1505.04597, 2015.

If you used the dataset in your work, please cite the original paper of it:

R. Souza, O. Lucena, J. Garrafa, D. Gobbi, M. Saluzzi, S. Appenzeller, L. Rittner, R. Frayne, and R. Lotufo, "An open, multi-vendor, multi-field-strength brain MR dataset and analysis of publicly available skull stripping methods agreement," NeuroImage, vol. 170, pp. 482-494, 2018, doi: 10.1016/j.neuroimage.2017.08.021.