FSTIF-UNet: A Deep Learning-Based Method Towards Automatic Segmentation of Intracranial Aneurysms in Un-Reconstructed 3D-RA-Reference-Cited by-同舟云学术

FSTIF-UNet: A Deep Learning-Based Method Towards Automatic Segmentation of Intracranial Aneurysms in Un-Reconstructed 3D-RA

Published:2023-08 Issue:8 Volume:27 Page:4028-4039
ISSN:2168-2194
Container-title:IEEE Journal of Biomedical and Health Informatics
language:
Short-container-title:IEEE J. Biomed. Health Inform.

Author:

Zhang Jianhua¹^ORCID,Zhao Yan¹^ORCID,Liu Xinke²^ORCID,Jiang Jincheng³,Li Youxiang²

Affiliation:

1. School of Mechanical Engineering, University of Science and Technology Beijing, Beijing, China

2. Department of Interventional Neuroradiology, Beijing Neurosurgical Institute and Beijing Tiantan Hospital, Capital Medical University, Beijing, China

3. School of Mechanical Engineering, Hebei University of Technology, Tianjin, China

Funder

National Natural Science Foundation of China

Natural Science Foundation of Hebei Province of China

Natural Science Foundation of Tianjin City of China

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

Health Information Management,Electrical and Electronic Engineering,Computer Science Applications,Health Informatics

Link

http://xplorestaging.ieee.org/ielx7/6221020/10210140/10130280.pdf?arnumber=10130280

Reference43 articles.

1. Fully automated detection and segmentation of intracranial aneurysms in subarachnoid hemorrhage on CTA using deep learning

2. Convolutional LSTM network: A machine learning approach for precipitation nowcasting;shi;Proc Int Conf Adv Neural Inf Process Syst,0

3. Surface-based 3D deep learning framework for segmentation of intracranial aneurysms from TOF-MRA images;yang,2020

4. Automatic Intracranial Aneurysm Segmentation Based on Spatial Information Fusion Feature from 3D-RA using U-Net

5. Deep Learning–Based Detection of Intracranial Aneurysms in 3D TOF-MRA

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