Dense biased networks with deep priori anatomy and hard region adaptation: Semi-supervised learning for fine renal artery segmentation

@article{He2020DenseBN,
  title={Dense biased networks with deep priori anatomy and hard region adaptation: Semi-supervised learning for fine renal artery segmentation},
  author={Yuting He and Guanyu Yang and Jian Yang and Y. Chen and Youyong Kong and Jiasong Wu and L. Tang and Xiaomei Zhu and J. Dillenseger and P. Shao and Shaobo Zhang and H. Shu and J. Coatrieux and S. Li},
  journal={Medical image analysis},
  year={2020},
  volume={63},
  pages={
          101722
        }
}
Fine renal artery segmentation on abdominal CT angiography (CTA) image is one of the most important tasks for kidney disease diagnosis and pre-operative planning. It will help clinicians locate each interlobar artery's blood-feeding region via providing the complete 3D renal artery tree masks. However, it is still a task of great challenges due to the large intra-scale changes, large inter-anatomy variation, thin structures, small volume ratio and small labeled dataset of the fine renal artery… Expand
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  • Computer Science, Medicine
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