This paper constructs a tree model by using the 3D stochastic and L system with brackets. Adjusting the range of the parameters, the present method ensures a favorable randomness and reflects the reality and the real ...This paper constructs a tree model by using the 3D stochastic and L system with brackets. Adjusting the range of the parameters, the present method ensures a favorable randomness and reflects the reality and the real time. Based on a simple mechanical model, the simulation of the phenomenon of wind swaying trees satisfies visual effects well.展开更多
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 mo...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%.展开更多
文摘This paper constructs a tree model by using the 3D stochastic and L system with brackets. Adjusting the range of the parameters, the present method ensures a favorable randomness and reflects the reality and the real time. Based on a simple mechanical model, the simulation of the phenomenon of wind swaying trees satisfies visual effects well.
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).
文摘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%.
