https://github.com/fzj-inm1-bda/pli2cells
Virtual Cresyl violet staining from 3D-PLI images.
https://github.com/fzj-inm1-bda/pli2cells
Last synced: 3 months ago
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Virtual Cresyl violet staining from 3D-PLI images.
- Host: GitHub
- URL: https://github.com/fzj-inm1-bda/pli2cells
- Owner: FZJ-INM1-BDA
- License: apache-2.0
- Created: 2025-01-17T16:28:11.000Z (9 months ago)
- Default Branch: main
- Last Pushed: 2025-05-20T11:12:14.000Z (5 months ago)
- Last Synced: 2025-06-09T02:04:47.003Z (4 months ago)
- Language: Python
- Size: 1000 KB
- Stars: 0
- Watchers: 3
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE.txt
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README
# Fibers to Cells
Virtual Cresyl violet staining from 3D-PLI.
## Quickstart
Clone the repository and install it using pip:
```bash
git clone https://jugit.fz-juelich.de/inm-1/bda/personal/aoberstrass/projects/pli2cells.git
cd pli2cells
pip install -e .
```
Installation requires `gxx_linux-64` and `openmpi` or `mpich` packages.
## Usage
Debugging the pipeline:
```bash
sbatch scripts/debug.sbatch
```
Full-scale training:
```bash
sbatch scripts/train.sbatch unetdftstyle_affine
```
where the `unetdftstyle_affine` experiment can be replaced with any configuration under `configs/experiment`.
To apply trained models adjust and run
```bash
sbatch scripts/apply_models.sbatch
```
## Project Organization
```
├── configs <- Hydra configuration files
│ ├── callbacks <- Callbacks configs
│ ├── datamodule <- Datamodule configs
│ ├── debug <- Debugging configs
│ ├── experiment <- Experiment configs
│ ├── hparams_search <- Hyperparameter search configs
│ ├── local <- Local configs
│ ├── log_dir <- Logging directory configs
│ ├── logger <- Logger configs
│ ├── model <- Model configs
│ ├── trainer <- Trainer configs
│ │
│ └── train.yaml <- Main config for training
│
├── data <- Project data
│ └── vervet1818-stained <- Repository containing train and test data
│
├── logs
│ ├── experiments <- Logs from experiments
│ ├── slurm <- Slurm outputs and errors
│ └── tensorboard/mlruns/... <- Training monitoring logs
│
├── scripts <- Scripts used in project
│ ├── apply_model.py <- Script to apply a trained model
│ ├── apply_models.sbatch <- Job submission to apply a model
│ ├── debug.sbatch <- Debug training job
│ ├── train.py <- Run training
│ └── train.sbatch <- Training job
│
├── src/pli_cyto <- Source code
│ ├── datamodules <- Lightning datamodules
│ ├── eval <- Code for evaluation scores
│ ├── models <- Lightning models
│ ├── utils <- Utility scripts
│ │
│ ├── testing_pipeline.py <- Model evaluation workflow
│ └── training_pipeline.py <- Model training workflow
│
├── LICENSE.txt <- Apache License Version 2.0
├── pyproject.toml <- Build configuration.
├── setup.cfg <- Declarative configuration of the project.
└── README.md <- This file
```
## DataLad
To retrieve the training data run
```bash
datalad get datasets/vervet1818-stained/
```
or
``` bash
datalad get --reckless=ephemeral datasets/vervet1818-stained
```
if you just want to link to the data on a remote without copying the files.
Additional sources of submodules are specified as `datalad.get.subdataset-source-candidate` in `.datalad/config` (See the [doc](http://handbook.datalad.org/en/latest/beyond_basics/101-148-clonepriority.html)).
Please note that access to the data can only be provided on request.
## How to Cite
If you use this work in your research, please cite it as follows:
```text
@article{oberstrass2025,
title={From Fibers to Cells: Fourier-Based Registration Enables Virtual Cresyl Violet Staining From 3D Polarized Light Imaging},
author={Oberstrass, Alexander and Vaca, Esteban and Upschulte, Eric and Niu, Meiqi and {Palomero-Gallagher}, Nicola and Graessel, David and Schiffer, Christian and Axer, Markus and Amunts, Katrin and Dickscheid, Timo},
journal={arXiv preprint arXiv:2505.11394},
year={2025}
}
```