---
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.