Automatic Retinogeniculate Visual Pathway Identification Based on Data-driven Fiber Clustering and Anatomical Constrains-Reference-Cited by-同舟云学术

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Automatic Retinogeniculate Visual Pathway Identification Based on Data-driven Fiber Clustering and Anatomical Constrains

Published:2023-07-24 Issue: Volume: Page:
ISSN:
Container-title:2023 42nd Chinese Control Conference (CCC)
language:
Short-container-title:

Author:

Zeng Qingrun¹,Zhang Jiawei¹,Chen Shengwei¹,Xie Lei¹,Huang Jiahao¹,He Jianzhong¹,Pan Yiang¹,Yu Jiangli¹,Hu Qiming¹,Amelina Evgeniya²,Amelin Mihail²,Feng Yuanjing¹

Affiliation:

1. College of Information Engineering, Zhejiang University of Technology,Hangzhou,China,310023

2. Novosibirsk State University,Novosibirsk,Russia

Funder

National Natural Science Foundation of China

Natural Science Foundation of Zhejiang Province

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx7/10239690/10239711/10239759.pdf?arnumber=10239759

Reference31 articles.

1. Overcoming Challenges of Cranial Nerve Tractography: A Targeted Review

2. Automated white matter fiber tract identification in patients with brain tumors

3. Partitioned shape modeling with on-the-flfly sparse appearance learning for anterior visual pathway segmentation;mansoor;Workshop on Clinical Image-Based Procedures,2015

4. Comparison of Diffusion-Weighted MRI Reconstruction Methods for Visualization of Cranial Nerves in Posterior Fossa Surgery

5. Semi-supervised region-connectivity-based cerebrovascular segmentation for time-of-flight magnetic resonance angiography image

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