metadata
license: mit
library_name: pytorch
pipeline_tag: image-segmentation
tags:
- medical-image-segmentation
- 3d-segmentation
- vessel-segmentation
- nnunet
- pytorch
HiLo: Spatial-Spectral Hybrid High-Low Frequency Activation for Heart and Brain Vessel Segmentation
HiLo is a 3D vessel segmentation framework for cardio-cerebrovascular imaging. It jointly models spatial and spectral high-/low-frequency cues to preserve fine tubular details while encoding large-scale anatomical structure.
This work is published in Expert Systems with Applications.
Weights
Usage
Please use these weights with the official code repository:
git clone https://github.com/deepang-ai/HiLo.git
cd HiLo
Follow the installation, data preparation, training, evaluation, and inference instructions in the HiLo README. HiLo depends on the bundled project-adapted nnUNet/ package in the code repository, so install it with:
pip install -e ./nnUNet
Citation
If you use HiLo or these pretrained weights, please cite:
@article{HUANG2026133453,
title = {HiLo: Spatial-Spectral Hybrid High-Low Frequency Activation for Heart and Brain Vessel Segmentation},
journal = {Expert Systems with Applications},
pages = {133453},
year = {2026},
issn = {0957-4174},
doi = {https://doi.org/10.1016/j.eswa.2026.133453},
url = {https://www.sciencedirect.com/science/article/pii/S0957417426023626},
author = {Jiahui Huang and Xin Lei and Qiong Wang and Valentin Sinitsyn and Yun Zhu and Ying Hu and Hao Chen and Yan Pang}
}
Please also cite nnU-Net when using the bundled training framework:
@article{isensee2021nnunet,
title={nnU-Net: a self-configuring method for deep learning-based biomedical image segmentation},
author={Isensee, Fabian and Jaeger, Paul F and Kohl, Simon AA and Petersen, Jens and Maier-Hein, Klaus H},
journal={Nature Methods},
volume={18},
number={2},
pages={203--211},
year={2021},
publisher={Nature Publishing Group}
}