---
language:
- en
pipeline_tag: image-segmentation
tags:
- medical
---

# BianqueNet

BianqueNet is a segmentation model based on DeepLabv3+ with additional modules designed to improve the segmentation accuracy with IVD-related areas from T2W MR images. It was introduced in the paper [Deep learning-based high-accuracy quantitation for lumbar intervertebral disc degeneration from MRI](https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8837609/) by Zheng et al. and first released in [this repository](https://github.com/no-saint-no-angel/BianqueNet). 

> Disclaimer: This model card was not written by the team that released the BianqueNet model.

## Intended uses & limitations
You can use this particular checkpoint on spine sagittal T2-weighted MRI images. See the model hub to look for other image segmentation models that might interest you.


## BibTeX entry and citation info

```bibtex
@article{zheng2022bianquenet,
  author = {Zheng, Hua-Dong and Sun, Yue-Li and Kong, De-Wei and Yin, Meng-Chen and Chen, Jiang and Lin, Yong-Peng and Ma, Xue-Feng and Wang, Hongshen and Yuan, Guang-Jie and Yao, Min and Cui, Xue-Jun and Tian, Ying-Zhong and Wang, Yong-Jun},
  year = 2022,
  pages = 841,
  title = {Deep learning-based high-accuracy quantitation for lumbar intervertebral disc degeneration from MRI},
  volume = 13,
  journal = {Nature Communications},
}
```