https://github.com/amrzhd/mriskullstripping

Developing a UNet3D model for accurate MRI skull stripping using the Calgary Campinas 359 dataset, enhancing neuroimaging preprocessing workflows.
https://github.com/amrzhd/mriskullstripping

biomedical-image-processing image-segmentation monai mri mri-images nibabel python pytorch unet unet-image-segmentation

Last synced: about 1 month ago
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Developing a UNet3D model for accurate MRI skull stripping using the Calgary Campinas 359 dataset, enhancing neuroimaging preprocessing workflows.

• Host: GitHub
• URL: https://github.com/amrzhd/mriskullstripping
• Owner: amrzhd
• License: mit
• Created: 2024-08-18T23:21:17.000Z (3 months ago)
• Default Branch: main
• Last Pushed: 2024-09-08T21:49:54.000Z (2 months ago)
• Last Synced: 2024-09-26T20:04:09.071Z (about 2 months ago)
• Topics: biomedical-image-processing, image-segmentation, monai, mri, mri-images, nibabel, python, pytorch, unet, unet-image-segmentation
• Language: Jupyter Notebook
• Homepage:
• Size: 20.5 KB
• Stars: 88
• Watchers: 1
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md
  • License: LICENSE

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README

        
# 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](https://portal.conp.ca/dataset?id=projects/calgary-campinas)

- 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.

![UNet3D Structure](https://drive.google.com/uc?export=view&id=1V6rL5vy5NQUCQxXu-xWdcCd-9f2kVmQu)

### 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.](https://arxiv.org/abs/1505.04597)

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.](https://www.sciencedirect.com/science/article/abs/pii/S1053811917306687?via%3Dihub)