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Akademický článek

An Automatic Segmentation Method of Cerebral Arterial Tree in TOF-MRA Based on DBCNet

Autor: ZHANG Jiajun, LU Yucheng, BAO Yifang, LI Yuxin, GENG Chen, HU Fuyuan, DAI Yakang

Publikováno v: Chinese Journal of Magnetic Resonance, Vol 40, Iss 03, Pp 320-331 (2023)

Arterial tree region segmentation from medical images of the brain is an early step in the diagnosis and evaluation of many cerebrovascular diseases. Most of the existing region segmentation methods rely on manual assistance. In this paper, we propos

Externí odkaz: https://doaj.org/article/561e6248b6a945289488f96a3d808f35

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Akademický článek

Automated computer-assisted detection system for cerebral aneurysms in time-of-flight magnetic resonance angiography using fully convolutional network

Autor: Geng Chen, Xia Wei, Huang Lei, Yang Liqin, Li Yuxin, Dai Yakang, Geng Daoying

Publikováno v: BioMedical Engineering OnLine, Vol 19, Iss 1, Pp 1-10 (2020)

Abstract Background As the rupture of cerebral aneurysm may lead to fatal results, early detection of unruptured aneurysms may save lives. At present, the contrast-unenhanced time-of-flight magnetic resonance angiography is one of the most commonly u

Externí odkaz: https://doaj.org/article/a48bb545257c4a2fa8ed16a2b002144e

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Akademický článek

Safe dynamic sparse training of modified RBF networks for joint feature selection and classification

Autor: Qian, Xusheng, Hu, Jisu, Zheng, Yi, Huang, He, Zhou, Zhiyong, Dai, Yakang

Publikováno v: In Neurocomputing 1 October 2024 600

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Akademický článek

Quantifying cerebral blood flow changes using arterial spin labeling: A comparative study of idiopathic rapid eye movement sleep behavior disorder and Parkinson's disease

Autor: Zhi, Yuqi, Chen, Mingshen, Zhou, Chunshan, Yang, Yongxu, Huang, Yan, Liang, Xiaoyun, Wang, Ping, Cheng, Xiaoyu, Mao, Chengjie, Jiang, Zhen, Dai, Yakang, Peng, Bo, Zhu, Jiangtao

Publikováno v: In Magnetic Resonance Imaging June 2024 109:158-164

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Crossover-Net: Leveraging the Vertical-Horizontal Crossover Relation for Robust Segmentation

Autor: Yu, Qian, Shi, Yinghuan, Zheng, Yefeng, Gao, Yang, Zhu, Jianbing, Dai, Yakang

Robust segmentation for non-elongated tissues in medical images is hard to realize due to the large variation of the shape, size, and appearance of these tissues in different patients. In this paper, we present an end-to-end trainable deep segmentati

Externí odkaz: http://arxiv.org/abs/2004.01397