Self-supervised segmentation using synthetic datasets via L-system
Juntao Huang
Xianhui Wu
Hongsheng Qi
School of Mathematical Sciences,University of Chinese Academy of Sciences,Beijing 100049,China;Academy of Mathematics and Systems Science,Chinese Academy of Sciences,Beijing 100190,China
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％.
机标关键词：l-systemdatasetssegmentationself-supervisedsyntheticusing
论文发表日期：2023-11-05
在线出版日期：2025-08-15（本平台首次上网日期，不代表文献的发表时间）
页数：9( 571-579 )
英文信息