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):

pip install -e /hpc/home/xz420/xingyuan/software/Halo

Run:

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.