{"id":50585594,"url":"https://github.com/baderlab/multiscale_human_liver_vem","last_synced_at":"2026-06-05T06:01:57.875Z","repository":{"id":339110325,"uuid":"1073187223","full_name":"BaderLab/Multiscale_human_liver_vEM","owner":"BaderLab","description":null,"archived":false,"fork":false,"pushed_at":"2026-02-18T03:08:29.000Z","size":44731,"stargazers_count":1,"open_issues_count":0,"forks_count":0,"subscribers_count":0,"default_branch":"main","last_synced_at":"2026-04-10T20:31:38.600Z","etag":null,"topics":[],"latest_commit_sha":null,"homepage":null,"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/BaderLab.png","metadata":{"files":{"readme":"README.md","changelog":null,"contributing":null,"funding":null,"license":"LICENSE.txt","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":"2025-10-09T18:32:58.000Z","updated_at":"2026-02-18T03:08:33.000Z","dependencies_parsed_at":null,"dependency_job_id":null,"html_url":"https://github.com/BaderLab/Multiscale_human_liver_vEM","commit_stats":null,"previous_names":["baderlab/multiscale_human_liver_vem"],"tags_count":0,"template":false,"template_full_name":null,"purl":"pkg:github/BaderLab/Multiscale_human_liver_vEM","repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/BaderLab%2FMultiscale_human_liver_vEM","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/BaderLab%2FMultiscale_human_liver_vEM/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/BaderLab%2FMultiscale_human_liver_vEM/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/BaderLab%2FMultiscale_human_liver_vEM/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/BaderLab","download_url":"https://codeload.github.com/BaderLab/Multiscale_human_liver_vEM/tar.gz/refs/heads/main","sbom_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/BaderLab%2FMultiscale_human_liver_vEM/sbom","scorecard":null,"host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":286080680,"owners_count":33932040,"icon_url":"https://github.com/github.png","version":null,"created_at":"2022-05-30T11:31:42.601Z","updated_at":"2026-05-26T15:22:16.424Z","status":"online","status_checked_at":"2026-06-05T02:00:06.157Z","response_time":120,"last_error":null,"robots_txt_status":"success","robots_txt_updated_at":"2025-07-24T06:49:26.215Z","robots_txt_url":"https://github.com/robots.txt","online":true,"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":"2026-06-05T06:01:57.125Z","updated_at":"2026-06-05T06:01:57.868Z","avatar_url":"https://github.com/BaderLab.png","language":"Jupyter Notebook","funding_links":[],"categories":[],"sub_categories":[],"readme":"# Multiscale Human Liver Volume Electron Microscopy\n\n## Introduction\n\nUsing a deep learning-based segmentation framework, we generated comprehensive labels across vascular, cellular, and subcellular levels, enabling quantitative analysis of bile duct–cholangiocyte organization and sinusoidal branch geometry. At the organelle scale, analysis of 35,790 mitochondria revealed distinct morphological profiles and spatial distributions. Examination of mitochondrial–endoplasmic reticulum (ER) spatial relationships uncovered characteristic ER-associated mitochondrial narrowing, indicative of fission and fusion activity.\n\n\u003cp align=\"center\"\u003e\n  \u003cimg src=\"figures/automated_segmentation.png\" alt=\"Automated segmentation overview\" width=\"800\"\u003e\n\u003c/p\u003e\n\n## Installation\n\n1. **Create a virtual environment** to install the required packages. This takes less than 1 min. An example setup script is provided in `create_run_example.slurm`.\n\n2. **Clone the repository:**\n   ```bash\n   git clone https://github.com/BaderLab/Multiscale_human_liver_vEM.git\n   ```\n\n3. **Install dependencies:**\n   ```bash\n   cd Multiscale_human_liver_vEM\n   pip install -r requirements.txt\n   ```\n   The `requirements.txt` includes packages needed for both [nnUNet](https://github.com/MIC-DKFZ/nnUNet) and [SAM2](https://github.com/facebookresearch/sam2).\n\n## Getting Started\n\n### 1. Vascular and Cellular Level Segmentation\n\nWe provide a script that uses SAM2 to generate 3D instance masks from input prompts (GPU is required and we used H100 GPU when running this):\n\n```bash\npython sam2maskpropagator.py\n```\n\n### 2. Organelle Segmentation\n\nOrganelle segmentation was performed using [nnUNet](https://github.com/MIC-DKFZ/nnUNet) with pretraining and fine-tuning (GPU is required and we used H100 GPU when running this). Trained model checkpoints for all segmented organelles are available on [Zenodo](https://zenodo.org/uploads/17360859).\n\n### 3. Mitochondrial Morphology Feature Extraction\n\nAfter obtaining organelle masks, morphological features of mitochondria were extracted using [PyRadiomics](https://pyradiomics.readthedocs.io/):\n\n```bash\npython morphology_features.py\n```\n\n### 4. Mitochondria–ER Interaction Analysis\n\nTo analyze mitochondria–ER spatial interactions:\n\n```bash\npython mito_er_analysis.py\n```\n\n## Acknowledgements\n\nWe thank the [SAM2](https://arxiv.org/abs/2408.00714) and [nnUNet](https://www.nature.com/articles/s41592-020-01008-z) teams for making their source code publicly available. We also thank the [PyRadiomics](https://doi.org/10.1158/0008-5472.CAN-17-0339) team for their open-source morphological feature extraction package. We gratefully acknowledge [OpenOrganelle](https://openorganelle.janelia.org/) and [Parlakgül et al. (2022)](https://www.nature.com/articles/s41586-022-04488-5) for making the mouse liver volume electron microscopy data publicly available.\n\n## Citation\n\n\u003c!-- Update this once the manuscript is published --\u003e\n```bibtex\n@article{multiscale_human_liver,\n  title   = {},\n  author  = {},\n  journal = {},\n  volume  = {},\n  pages   = {},\n  year    = {}\n}\n```\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fbaderlab%2Fmultiscale_human_liver_vem","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fbaderlab%2Fmultiscale_human_liver_vem","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fbaderlab%2Fmultiscale_human_liver_vem/lists"}