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license: mit
library_name: halo
pipeline_tag: image-segmentation
tags:
- spatial
- xenium
- cell-segmentation
- cellpose
- microscopy
- bioimage
---
# Halo
Halo is a lightweight pipeline that takes a Xenium dataset folder, builds a 2-channel preprocessing image (DAPI + transcript density), runs Cellpose with the `Halo` pretrained model name, and outputs a cell mask file.
## Model Description
Halo is a wrapper pipeline around Xenium preprocessing and Cellpose inference. It is intended for whole-image inference without tiling.
## Intended Use
- Xenium DAPI + transcript density preprocessing
- Whole-image cell segmentation using Cellpose
## Inputs
- Xenium dataset directory containing morphology images and transcript tables
- DAPI image auto-detected from `morphology_focus/ch0000_dapi.ome.tif` or `morphology.ome.tif`
## Outputs
- `halo_processed.tiff` (2-channel DAPI + transcript density)
- `cell_masks.npy` (default) or `cell_masks.tiff`
## Usage
Install (editable):
```bash
pip install -e /hpc/home/xz420/xingyuan/software/Halo
```
Run:
```bash
halo /path/to/xenium_dataset \
--out-dir /path/to/output \
--mask-format npy
```
If `--out-dir` is omitted, outputs are written to the current working directory.
## Parameters
- `--mask-format` set to `npy` or `tiff`
- `--processed-out` and `--mask-out` to override output filenames
- `--cpu` to force CPU inference
## Limitations
- Full-image inference can require substantial RAM and GPU memory on large Xenium images
- Assumes Xenium coordinate system and transcript columns `x`, `y`, `qv`, and `feature_name`
## Citation
If you use this pipeline in academic work, please cite Cellpose and Xenium references appropriate to your study.
## Contact
For questions or improvements, open an issue in the repository.
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