Self-supervised segmentation using synthetic datasets via L-system  

作　　者：Juntao Huang Xianhui Wu Hongsheng Qi 

机构地区：[1]School of Mathematical Sciences,University of Chinese Academy of Sciences,Beijing,100049,China [2]Academy of Mathematics and Systems Science,Chinese Academy of Sciences,Beijing,100190,China

出　　处：《Control Theory and Technology》2023年第4期571-579,共9页控制理论与技术（英文版）

基　　金：supported in part by the National Key Research and Development Program of China(No.2022YFA1004703);the National Natural Science Foundation of China(No.61873262).

摘　　要：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%.

关 键 词：L-SYSTEM Adversarial learning Vessel segmentation 

分 类 号：TP181[自动化与计算机技术—控制理论与控制工程]