| --- |
| language: |
| - en |
| pipeline_tag: image-segmentation |
| tags: |
| - medical |
| --- |
| |
| # BianqueNet |
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| 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). |
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| > Disclaimer: This model card was not written by the team that released the BianqueNet model. |
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| ## 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. |
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| ## BibTeX entry and citation info |
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|
| ```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}, |
| } |
| ``` |
