HIFUNet: Multi-Class Segmentation of Uterine Regions From MR Images Using Global Convolutional Networks for HIFU Surgery Planning-Reference-Cited by-全球学者库

HIFUNet: Multi-Class Segmentation of Uterine Regions From MR Images Using Global Convolutional Networks for HIFU Surgery Planning

Author:

Zhang Chen,Shu Huazhong^ORCID,Yang Guanyu,Li Faqi,Wen Yingang,Zhang Qin,Dillenseger Jean-Louis,Coatrieux Jean-Louis

Funder

National Natural Science Foundation

National Key Research and Development Program of China

Excellence Project Funds of Southeast University

Science Foundation for The Excellent Youth Scholars of Southeast University

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

Electrical and Electronic Engineering,Computer Science Applications,Radiological and Ultrasound Technology,Software

Link

Cited by 21 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

3. Comparative Assessment of deep learning methods for Prediction of Uterine Fibroid;2023 International Conference on Research Methodologies in Knowledge Management, Artificial Intelligence and Telecommunication Engineering (RMKMATE);2023-11-01

5. An Investigation of Segment Anything Model (SAM) on Uterus Segmentation;Proceedings of the 2023 8th International Conference on Biomedical Signal and Image Processing;2023-07-21