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Self-supervised segmentation using synthetic datasets via L-system

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Abstract

Vessel segmentation plays a crucial role in the diagnosis of many diseases, as well as assisting surgery. With the development of deep learning, many segmentation methods have been proposed, and the results have become more and more accurate. However, most of these methods are based on supervised learning, which require a large amount of labeled data as training data. To overcome this shortcoming, unsupervised and self-supervised methods have also received increasing attention. In this paper, we generate a synthetic training datasets through L-system, and utilize adversarial learning to narrow the distribution difference between the generated data and the real data to obtain the ultimate network. Our method achieves state-of-the-art (SOTA) results on X-ray angiography artery disease (XCAD) by a large margin of nearly 10.4%.

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Data Availability

The data that support the finding of this study are openly available in Ref. [4] and Ref. [25].

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Authors and Affiliations

  1. School of Mathematical Sciences, University of Chinese Academy of Sciences, Bei**g, 100049, China

    Juntao Huang, **anhui Wu & Hongsheng Qi

  2. Academy of Mathematics and Systems Science, Chinese Academy of Sciences, Bei**g, 100190, China

    Juntao Huang, **anhui Wu & Hongsheng Qi

Authors
  1. Juntao Huang
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  2. **anhui Wu
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  3. Hongsheng Qi
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Correspondence to Hongsheng Qi.

Additional information

This work was supported in part by the National Key Research and Development Program of China (No. 2022YFA1004703) and the National Natural Science Foundation of China (No. 61873262).

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Huang, J., Wu, X. & Qi, H. Self-supervised segmentation using synthetic datasets via L-system. Control Theory Technol. 21, 571–579 (2023). https://doi.org/10.1007/s11768-023-00151-0

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