{"id":14958107,"url":"https://github.com/amrzhd/mriskullstripping","last_synced_at":"2025-04-07T17:11:07.891Z","repository":{"id":253709010,"uuid":"844282832","full_name":"amrzhd/MRISkullStripping","owner":"amrzhd","description":"Developing a UNet3D model for accurate MRI skull stripping using the Calgary Campinas 359 dataset, enhancing neuroimaging preprocessing workflows.","archived":false,"fork":false,"pushed_at":"2024-11-20T07:49:58.000Z","size":62,"stargazers_count":94,"open_issues_count":0,"forks_count":2,"subscribers_count":1,"default_branch":"main","last_synced_at":"2025-03-31T15:19:07.099Z","etag":null,"topics":["biomedical-image-processing","image-segmentation","monai","mri","mri-images","nibabel","python","pytorch","unet","unet-image-segmentation"],"latest_commit_sha":null,"homepage":"","language":"Jupyter Notebook","has_issues":true,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":"mit","status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/amrzhd.png","metadata":{"files":{"readme":"README.md","changelog":null,"contributing":null,"funding":null,"license":"LICENSE","code_of_conduct":null,"threat_model":null,"audit":null,"citation":null,"codeowners":null,"security":null,"support":null,"governance":null,"roadmap":null,"authors":null,"dei":null,"publiccode":null,"codemeta":null}},"created_at":"2024-08-18T23:21:17.000Z","updated_at":"2025-03-17T12:16:38.000Z","dependencies_parsed_at":"2024-08-19T00:28:54.339Z","dependency_job_id":"aef917a2-922f-4671-aad2-f0810c213272","html_url":"https://github.com/amrzhd/MRISkullStripping","commit_stats":{"total_commits":7,"total_committers":1,"mean_commits":7.0,"dds":0.0,"last_synced_commit":"4d1b77839c417f8d942fe680a323cf7d06642b23"},"previous_names":["amrzhd/mriskullstripping-"],"tags_count":0,"template":false,"template_full_name":null,"repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/amrzhd%2FMRISkullStripping","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/amrzhd%2FMRISkullStripping/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/amrzhd%2FMRISkullStripping/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/amrzhd%2FMRISkullStripping/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/amrzhd","download_url":"https://codeload.github.com/amrzhd/MRISkullStripping/tar.gz/refs/heads/main","host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":247694876,"owners_count":20980733,"icon_url":"https://github.com/github.png","version":null,"created_at":"2022-05-30T11:31:42.601Z","updated_at":"2022-07-04T15:15:14.044Z","host_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub","repositories_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories","repository_names_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repository_names","owners_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners"}},"keywords":["biomedical-image-processing","image-segmentation","monai","mri","mri-images","nibabel","python","pytorch","unet","unet-image-segmentation"],"created_at":"2024-09-24T13:16:14.314Z","updated_at":"2025-04-07T17:11:07.867Z","avatar_url":"https://github.com/amrzhd.png","language":"Jupyter Notebook","funding_links":[],"categories":[],"sub_categories":[],"readme":"# MRI Skull Stripping Using UNet3D\n\n## Project Overview\n\nThis 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.\n\n## Dataset\nT1-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)\n- 359 Participants\n- Acquisition matrix size 256 x 218 x [170,180]\n- 1.5 T and 3 T Magnetic Field Strength\n- Voxel size for images is 1 mm³\n  \n## Model\nThe 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.\n\n![UNet3D Structure](https://drive.google.com/uc?export=view\u0026id=1V6rL5vy5NQUCQxXu-xWdcCd-9f2kVmQu)\n\n### Training Setup\n- Optimizer: Adam\n- Batch size: 64\n- Epochs: 500\n- Learning Rate: 0.001\n- Loss Function: Cross Entropy\n\n## Requirements\nTo run the code in this repository, make sure you have the following dependencies installed:\n- Python \u003e=3.8\n- PyTorch == 2.3 (verified working with 2.0 - 2.3, both for CPU and GPU)\n- torch-summary == 1.4.5\n- nibabel == 5.2.1\n- monai == 1.3.2\n- scikit-learn \u003e= 0.20.1\n- matplotlib \u003e= 2.2.3\n  \n## References\nIf you use this code or the UNet model architecture in your work, please cite the original paper of the orignal model:\n\n[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)\n\nIf you used the dataset in your work, please cite the original paper of it:\n\n[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)\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Famrzhd%2Fmriskullstripping","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Famrzhd%2Fmriskullstripping","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Famrzhd%2Fmriskullstripping/lists"}