{"id":37670990,"url":"https://github.com/cbica/deepmrseg","last_synced_at":"2026-01-16T12:04:19.715Z","repository":{"id":45071442,"uuid":"333213835","full_name":"CBICA/DeepMRSeg","owner":"CBICA","description":null,"archived":false,"fork":false,"pushed_at":"2023-04-03T15:54:25.000Z","size":190,"stargazers_count":7,"open_issues_count":5,"forks_count":7,"subscribers_count":2,"default_branch":"main","last_synced_at":"2025-09-09T23:13:10.984Z","etag":null,"topics":[],"latest_commit_sha":null,"homepage":null,"language":"Python","has_issues":true,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":"other","status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/CBICA.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,"zenodo":null,"notice":null,"maintainers":null,"copyright":null,"agents":null,"dco":null,"cla":null}},"created_at":"2021-01-26T20:50:57.000Z","updated_at":"2024-10-25T15:01:09.000Z","dependencies_parsed_at":"2025-09-09T21:34:20.625Z","dependency_job_id":"0e908d7d-38d8-4be7-8829-2d682a280240","html_url":"https://github.com/CBICA/DeepMRSeg","commit_stats":null,"previous_names":[],"tags_count":2,"template":false,"template_full_name":null,"purl":"pkg:github/CBICA/DeepMRSeg","repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/CBICA%2FDeepMRSeg","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/CBICA%2FDeepMRSeg/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/CBICA%2FDeepMRSeg/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/CBICA%2FDeepMRSeg/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/CBICA","download_url":"https://codeload.github.com/CBICA/DeepMRSeg/tar.gz/refs/heads/main","sbom_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/CBICA%2FDeepMRSeg/sbom","scorecard":null,"host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":286080680,"owners_count":28478424,"icon_url":"https://github.com/github.png","version":null,"created_at":"2022-05-30T11:31:42.601Z","updated_at":"2026-01-16T11:59:17.896Z","status":"ssl_error","status_checked_at":"2026-01-16T11:55:55.838Z","response_time":107,"last_error":"SSL_read: unexpected eof while reading","robots_txt_status":"success","robots_txt_updated_at":"2025-07-24T06:49:26.215Z","robots_txt_url":"https://github.com/robots.txt","online":false,"can_crawl_api":true,"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":[],"created_at":"2026-01-16T12:04:19.629Z","updated_at":"2026-01-16T12:04:19.694Z","avatar_url":"https://github.com/CBICA.png","language":"Python","funding_links":[],"categories":[],"sub_categories":[],"readme":"# DeepMRSeg\n\n[![Codacy Badge](https://api.codacy.com/project/badge/Grade/abb2c20d73ed464180494bf2fed3f0eb)](https://app.codacy.com/gh/CBICA/DeepMRSeg?utm_source=github.com\u0026utm_medium=referral\u0026utm_content=CBICA/DeepMRSeg\u0026utm_campaign=Badge_Grade_Settings)\n\nDeepMRSeg is a Python-based package for MRI image segmentation. The package is developed and maintained by the [Center for Biomedical Image Computing and Analytics (CBICA)](https://www.cbica.upenn.edu/) at the University of Pennsylvania. As the name implies, main modules of DeepMRSeg are built upon Deep Learning models that perform a set of image segmentation steps on MRI scans.\n\nDeepMRSeg aims to provide users a ***robust***, ***accurate*** and ***user-friendly*** toolset for performing common segmentation tasks in neuroimaging. In order to meet these challenges, the development of DeepMRSeg was guided by the following set of fundamental principles:\n\n* ***Efficient network architecture:*** DeepMRSeg uses a modified UNet architecture that combines an ensemble of learners for a robust segmentation  _[1]_.\n* ***Model repository with extensively trained models:*** We provide a set of pre-trained models for various segmentation tasks. We applied model training using ***_very large and diverse MRI datasets_*** with carefully curated and verified ground-truth labels.\n* ***Easy installation and application:*** Using a few simple commands, users can easily install DeepMRSeg on different platforms, download pre-trained models, and apply these models on their images. Most applications require no or minimal pre-processing; thus, users can directly apply them on raw scans.\n* ***Extensibility:*** DeepMRSeg is built using a generic network architecture and a software package that allows extending it with minimal effort. The model repository will grow in the future with regular addition of new models and tasks.\n\n## Supported Platforms\nWe have tested DeepMRSeg on the following platforms: \n-   Windows 10 Enterprise x64\n-   Ubuntu 18.04.3 , 20.04.2\nIt may also work on other platforms.\n\n## Prerequisities\n-   [Python 3](https://www.python.org/downloads/)\n-   If you prefer conda, you may install it from [here](https://www.anaconda.com/products/individual)\n\n## Installation Instructions\nTo setup your GPU for use with tensorflow, please follow the tensorflow guide for [GPU support](https://www.tensorflow.org/install/gpu).\n\n### 1) Direct installation at default location \n```\ngit clone  https://github.com/CBICA/DeepMRSeg.git\ncd DeepMRSeg\npython setup.py install #install DeepMRSeg and its dependencies\n```\n\n### 2) Installation in conda environment\n```\nconda create --name DeepMRSeg python=3.7.9\nconda activate DeepMRSeg\n```\nThen follow steps from [direct installation](#direct-installation-at-default-location)\n\n## Usage\n\nAfter installation of the package, users can call DeepMRSeg commands on the command prompt (or on Anaconda prompt).\n\n### Pre-trained models\n\nPre-trained models for testing are hosted in [DeepMRSeg-Models repository](https://github.com/CBICA/DeepMRSeg-Models). Users can manually download a model from the model repository into a local folder.\n\nAlternatively, the model can be downloaded to a pre-defined local folder (_~/.deepmrseg/trained_models_) automatically using the command:\n\n```\ndeepmrseg_downloadmodel\n```\n\n### Training and testing\n\nUsers can train their own model using a custom training dataset (training):\n\n```\ndeepmrseg_train\n```\n\nor apply a pre-trained model on their image(s) (testing):\n\n```\ndeepmrseg_test \n```\n\nNote that _deepmrseg_train_ and _deepmrseg_test_ are generic commands that allow users to run training and testing in an exhaustive way by supplying a set of user arguments.\n\n### Applying a task\n\nAlternatively, we provide a simplified interface for the application of a specific segmentation task on user data:\n\n```\ndeepmrseg_apply\n```\n\nNote that _deepmrseg_apply_ is a wrapper to _deepmrseg_test_, which calls it with a pre-defined model automatically downloaded using _deepmrseg_downloadmodel_.\n\n## Examples\n\nWe provide here few examples using minimal argument sets as a quick reference. These examples also show 3 possible I/O options provided for different use cases (single subject, batch processing using an image list and batch processing of images in a folder).\n\n### Testing\n\nQuick application on single subject using the wrapper scripts and a pre-trained model:\n\n```\n# Download the pre-trained ICV segmentation model\ndeepmrseg_downloadmodel --model dlicv  \n\n# Segment image\ndeepmrseg_apply --task dlicv --inImg subj1_T1.nii.gz --outImg subj1_T1_DLICV.nii.gz\n```\n\nQuick application for batch processing using an image list: \n\n```\n#   User provides a csv file with columns: ID,InputMod1,InputMod2,...,OutputImage\ndeepmrseg_apply --task dlicv --sList subjectList.csv\n```\n\nQuick application for batch processing of all images in a folder:\n\n```\n#   Testing is applied individually to all images with the given suffix in the input folder\ndeepmrseg_apply --task dlicv --inDir myindir --outDir myoutdir --inSuff _T1.nii.gz --outSuff _DLICV.nii.gz\n\n```\n\nSegmentation using the deepmrseg_test command:\n```\n#    \ndeepmrseg_test --mdldir my/path/to/pretrained/model --sList subjectList.csv\n```\nPlease see the user manual (or call the command with the -h option) for details of the complete command line arguments for deepmrseg_test.\n\n### Training\n\nTraining a new model using the deepmrseg_train command:\n```\n#    \ndeepmrseg_train --sList subjectList.csv --mdlDir my/path/to/output/model \n```\nPlease see the user manual (or call the command with the -h option) for details of the complete command line arguments for deepmrseg_train.\n\n\n## License\n\n## How to cite DeepMRSeg\n\n## Publications\n\n_[1] Doshi, Jimit, et al. DeepMRSeg: A convolutional deep neural network for anatomy and abnormality segmentation on MR images. arXiv preprint arXiv:1907.02110 (2019)_.\n\n## Authors and Contributors\n\nThe DeepMRSeg package is currently developed and maintained by:\n\nOthers who contributed to the project are:\n\n## Grant support\n\nDevelopment of the DeepMRSeg package is supported by the following grants:\n\n## Disclaimer\n-   The software has been designed for research purposes only and has neither been reviewed nor approved for clinical use by the Food and Drug Administration (FDA) or by any other federal/state agency.\n-   This code (excluding dependent libraries) is governed by the license provided in https://www.med.upenn.edu/cbica/software-agreement-non-commercial.html unless otherwise specified.\n-   By using DeepMRSeg, you agree to the license as stated in the [license file](https://github.com/CBICA/DeepMRSeg/blob/main/LICENSE).\n\n## Contact\nFor more information, please contact \u003ca href=\"mailto:software@cbica.upenn.edu\"\u003eCBICA Software\u003c/a\u003e.\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fcbica%2Fdeepmrseg","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fcbica%2Fdeepmrseg","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fcbica%2Fdeepmrseg/lists"}