{"id":34795865,"url":"https://github.com/mu40/babyseg","last_synced_at":"2026-05-23T08:02:15.102Z","repository":{"id":319862704,"uuid":"1079230681","full_name":"mu40/babyseg","owner":"mu40","description":"Brain segmentation across the first years of life","archived":false,"fork":false,"pushed_at":"2026-02-18T13:04:14.000Z","size":200,"stargazers_count":4,"open_issues_count":0,"forks_count":0,"subscribers_count":1,"default_branch":"main","last_synced_at":"2026-02-18T17:36:48.406Z","etag":null,"topics":["brain-segmentation","deep-learning","domain-randomization","mri","neuroimaging","nifti"],"latest_commit_sha":null,"homepage":"https://babyseg.io","language":"Python","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/mu40.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":"2025-10-19T11:46:47.000Z","updated_at":"2026-02-18T13:04:17.000Z","dependencies_parsed_at":"2025-10-20T16:36:02.121Z","dependency_job_id":null,"html_url":"https://github.com/mu40/babyseg","commit_stats":null,"previous_names":["mu40/babyseg"],"tags_count":0,"template":false,"template_full_name":null,"purl":"pkg:github/mu40/babyseg","repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/mu40%2Fbabyseg","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/mu40%2Fbabyseg/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/mu40%2Fbabyseg/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/mu40%2Fbabyseg/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/mu40","download_url":"https://codeload.github.com/mu40/babyseg/tar.gz/refs/heads/main","sbom_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/mu40%2Fbabyseg/sbom","scorecard":null,"host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":286080680,"owners_count":33387656,"icon_url":"https://github.com/github.png","version":null,"created_at":"2022-05-30T11:31:42.601Z","updated_at":"2026-05-23T04:15:53.637Z","status":"ssl_error","status_checked_at":"2026-05-23T04:15:53.242Z","response_time":53,"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":["brain-segmentation","deep-learning","domain-randomization","mri","neuroimaging","nifti"],"created_at":"2025-12-25T10:46:56.722Z","updated_at":"2026-05-23T08:02:15.097Z","avatar_url":"https://github.com/mu40.png","language":"Python","funding_links":[],"categories":[],"sub_categories":[],"readme":"# BabySeg\n\nBabySeg is a brain segmentation tool for infants and young children, designed to delineate anatomical structures in MRI without preprocessing.\nThe tool can integrate information from multiple NIfTI image volumes of variable contrast, shape, and resolution in any order, provided that (1) their header geometries are correct, and (2) they are properly aligned in world space.\n\n\n## Running BabySeg\n\nThe recommended way to run BabySeg is in a [container](docker/README.md).\n\n\n## Video\n\nWatch a 30-minute [video](https://www.youtube.com/watch?v=UZwUZQXhnBo) recording of a talk introducing the motivation and methodology behind BabySeg.\n\n\n## Attribution\n\nIf you find this work useful, please cite the relevant papers below.\n\nBabySeg [method](https://arxiv.org/abs/2512.05114):\n\n```bibtex\n@inproceedings{hoffmann2025deep,\n  title={{Deep infant brain segmentation from multi-contrast MRI}},\n  author={Hoffmann, Malte and Z{\\\"o}llei, Lilla and Dalca, Adrian V},\n  booktitle={{Asilomar Conference on Signals, Systems, and Computers}},\n  pages={974--981},\n  year={2025},\n  publisher={IEEE}\n}\n```\n\nData [engine](https://arxiv.org/abs/2507.13458):\n\n```bibtex\n@article{hoffmann2025domain,\n  title={Domain-randomized deep learning for neuroimage analysis},\n  author={Hoffmann, Malte},\n  journal={IEEE Signal Processing Magazine},\n  volume={42},\n  number={4},\n  pages={78--90},\n  year={2025},\n  publisher={IEEE}\n}\n```\n\n## Support\n\nRead the [FAQ](doc/faq.md), post questions to the FreeSurfer [mailing list](https://surfer.nmr.mgh.harvard.edu/fswiki/FreeSurferSupport), or file bugs on GitHub.\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fmu40%2Fbabyseg","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fmu40%2Fbabyseg","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fmu40%2Fbabyseg/lists"}