https://github.com/baderlab/multiscale_human_liver_vem
https://github.com/baderlab/multiscale_human_liver_vem
Last synced: about 1 month ago
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- Host: GitHub
- URL: https://github.com/baderlab/multiscale_human_liver_vem
- Owner: BaderLab
- License: mit
- Created: 2025-10-09T18:32:58.000Z (9 months ago)
- Default Branch: main
- Last Pushed: 2026-02-18T03:08:29.000Z (5 months ago)
- Last Synced: 2026-04-10T20:31:38.600Z (3 months ago)
- Language: Jupyter Notebook
- Size: 42.7 MB
- Stars: 1
- Watchers: 0
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE.txt
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README
# Multiscale Human Liver Volume Electron Microscopy
## Introduction
Using 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.
## Installation
1. **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`.
2. **Clone the repository:**
```bash
git clone https://github.com/BaderLab/Multiscale_human_liver_vEM.git
```
3. **Install dependencies:**
```bash
cd Multiscale_human_liver_vEM
pip install -r requirements.txt
```
The `requirements.txt` includes packages needed for both [nnUNet](https://github.com/MIC-DKFZ/nnUNet) and [SAM2](https://github.com/facebookresearch/sam2).
## Getting Started
### 1. Vascular and Cellular Level Segmentation
We 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):
```bash
python sam2maskpropagator.py
```
### 2. Organelle Segmentation
Organelle 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).
### 3. Mitochondrial Morphology Feature Extraction
After obtaining organelle masks, morphological features of mitochondria were extracted using [PyRadiomics](https://pyradiomics.readthedocs.io/):
```bash
python morphology_features.py
```
### 4. Mitochondria–ER Interaction Analysis
To analyze mitochondria–ER spatial interactions:
```bash
python mito_er_analysis.py
```
## Acknowledgements
We 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.
## Citation
```bibtex
@article{multiscale_human_liver,
title = {},
author = {},
journal = {},
volume = {},
pages = {},
year = {}
}
```