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--- |
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language: |
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- en |
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pipeline_tag: image-segmentation |
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tags: |
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- medical |
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--- |
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# 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 |
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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 |
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@article{zheng2022bianquenet, |
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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}, |
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year = 2022, |
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pages = 841, |
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title = {Deep learning-based high-accuracy quantitation for lumbar intervertebral disc degeneration from MRI}, |
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volume = 13, |
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journal = {Nature Communications}, |
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} |
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``` |
