Fully automatic deep learning trained on limited data for carotid artery segmentation from large image volumes | Zendy

Tianshu Zhou | Zendy; Tao Tan | Zendy; Xiaoyan Pan | Zendy; Hui Tang | Zendy; Jingsong Li | Zendy

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Fully automatic deep learning trained on limited data for carotid artery segmentation from large image volumes

Author(s) -

Tianshu Zhou,

Tao Tan,

Xiaoyan Pan,

Hui Tang,

Jingsong Li

Publication year - 2020

Publication title -

quantitative imaging in medicine and surgery

Language(s) - English

Resource type - Journals

SCImago Journal Rank - 0.766

H-Index - 21

eISSN - 2223-4292

pISSN - 2223-4306

DOI - 10.21037/qims-20-286

Subject(s) - deep learning , artificial intelligence , computer science , segmentation , pipeline (software) , sørensen–dice coefficient , voxel , pattern recognition (psychology) , similarity (geometry) , image segmentation , computer vision , image (mathematics) , programming language

The objectives of this study were to develop a 3D convolutional deep learning framework (CarotidNet) for fully automatic segmentation of carotid bifurcations in computed tomography angiography (CTA) images and to facilitate the quantification of carotid stenosis and risk assessment of stroke.

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