{"id":19417141,"url":"https://github.com/pegah-ardehkhani/brain-mri-segmentation","last_synced_at":"2026-05-09T07:39:54.276Z","repository":{"id":44592872,"uuid":"512875115","full_name":"Pegah-Ardehkhani/Brain-MRI-Segmentation","owner":"Pegah-Ardehkhani","description":"U-Net from Scratch for Brain Tumor Segmentation","archived":false,"fork":false,"pushed_at":"2022-07-11T19:57:05.000Z","size":8391,"stargazers_count":3,"open_issues_count":0,"forks_count":0,"subscribers_count":1,"default_branch":"main","last_synced_at":"2025-01-07T17:18:32.966Z","etag":null,"topics":["ai-for-medical-diagnosis","brain-imaging","brain-mri","brain-mri-images","deep-learning","deep-neural-networks","dice-coefficient","healthcare-imaging","iou","medical-image-processing","medical-imaging","mri","mri-brain","mri-brain-segmentation","mri-images","python","segmentation","tensorflow","u-net","u-net-keras"],"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/Pegah-Ardehkhani.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}},"created_at":"2022-07-11T18:54:07.000Z","updated_at":"2024-12-29T11:30:55.000Z","dependencies_parsed_at":"2022-08-03T18:01:13.677Z","dependency_job_id":null,"html_url":"https://github.com/Pegah-Ardehkhani/Brain-MRI-Segmentation","commit_stats":null,"previous_names":[],"tags_count":1,"template":false,"template_full_name":null,"repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/Pegah-Ardehkhani%2FBrain-MRI-Segmentation","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/Pegah-Ardehkhani%2FBrain-MRI-Segmentation/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/Pegah-Ardehkhani%2FBrain-MRI-Segmentation/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/Pegah-Ardehkhani%2FBrain-MRI-Segmentation/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/Pegah-Ardehkhani","download_url":"https://codeload.github.com/Pegah-Ardehkhani/Brain-MRI-Segmentation/tar.gz/refs/heads/main","host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":240595273,"owners_count":19826350,"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":["ai-for-medical-diagnosis","brain-imaging","brain-mri","brain-mri-images","deep-learning","deep-neural-networks","dice-coefficient","healthcare-imaging","iou","medical-image-processing","medical-imaging","mri","mri-brain","mri-brain-segmentation","mri-images","python","segmentation","tensorflow","u-net","u-net-keras"],"created_at":"2024-11-10T13:07:26.604Z","updated_at":"2026-05-09T07:39:49.242Z","avatar_url":"https://github.com/Pegah-Ardehkhani.png","language":"Jupyter Notebook","funding_links":[],"categories":[],"sub_categories":[],"readme":"# Brain MRI Segmentation 🧠 ![license](https://img.shields.io/github/license/Pegah-Ardehkhani/Brain-MRI-Segmentation.svg) ![releases](https://img.shields.io/github/release/Pegah-Ardehkhani/Brain-MRI-Segmentation.svg) \u003ca href=\"https://colab.research.google.com/github/Pegah-Ardehkhani/Brain-MRI-Segmentation/blob/main/Brain%20MRI%20Segmentation.ipynb\" target=\"_parent\\\"\u003e\u003cimg src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/\u003e\u003c/a\u003e [![nbviewer](https://img.shields.io/badge/render-nbviewer-orange.svg)](http://nbviewer.org/github/Pegah-Ardehkhani/Brain-MRI-Segmentation/blob/main/Brain%20MRI%20Segmentation.ipynb)\n\n\u003cp align=\"center\"\u003e\n  \u003cimg width=\"500\" height=\"300\" src=\"https://mateuszbuda.github.io/images/brainseg/CS_6668.gif\"\u003e\n\u003c/p\u003e\n\n## Dataset 📔\n\n[Kaggle link: Brain Tumor Data](https://www.kaggle.com/mateuszbuda/lgg-mri-segmentation)\n\nThe dataset used for development was obtained from The Cancer Imaging Archive (TCIA) and involved 110 cases of lower-grade glioma patients. Registers brain MR images with manual FLAIR abnormality segmentation masks are published as a Kaggle Dataset lgg-mri-segmentation.\n\n## Project Overview \n\n**U-net:**\n\nU-net from scratch has been written.\n\nKey aspects of U-Net:\n\n1. Convolution Layers: Convolution operation are used to learn information from images which then can be used as features for machine learning problems.\n\n2. Down Sampling: Sequence of convolution combined with max pooling results in down sampling. In down sampling, size of the image is reduced which means we can observe larger portion of image in a single convolution operation. Down sampling is a good approach for identifying what is present in the image but for identifying where the object is we need to use upsampling.\n\n3. Up Sampling: It is just opposite of down sampling. We go from low resolution to high resolution. For up sampling U-Net uses transposed covolution which is achieved by taking transpose of filter kernels and reversing the process of convolution.\n\nFollowing picture gives a clear picture of What a U-Net is.\n\n\u003cp align=\"center\"\u003e\n  \u003cimg width=\"1000\" height=\"500\" src=\"https://theaisummer.com/static/3995761ad87f8909f5dec5925d182e80/4ff83/The-3D-Unet-model.png\"\u003e\n\u003c/p\u003e\n\n**Reustls:**\n\n\u003cp align=\"center\"\u003e\n  \u003cimg width=\"800\" height=\"400\" src=\"https://github.com/Pegah-Ardehkhani/Brain-MRI-Segmentation/blob/main/model%20history.PNG\"\u003e\n\u003c/p\u003e\n\nDice Score on the test data: 0.9027\n\n\u003cp align=\"center\"\u003e\n  \u003cimg width=\"1000\" height=\"300\" src=\"https://github.com/Pegah-Ardehkhani/Brain-MRI-Segmentation/blob/main/original%20and%20pred.PNG\"\u003e\n\u003c/p\u003e\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fpegah-ardehkhani%2Fbrain-mri-segmentation","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fpegah-ardehkhani%2Fbrain-mri-segmentation","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fpegah-ardehkhani%2Fbrain-mri-segmentation/lists"}