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Erschienen in:
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2020 | OriginalPaper | Buchkapitel

JointVesselNet: Joint Volume-Projection Convolutional Embedding Networks for 3D Cerebrovascular Segmentation

verfasst von : Yifan Wang, Guoli Yan, Haikuan Zhu, Sagar Buch, Ying Wang, Ewart Mark Haacke, Jing Hua, Zichun Zhong

Erschienen in: Medical Image Computing and Computer Assisted Intervention – MICCAI 2020

Verlag: Springer International Publishing

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Abstract

In this paper, we present an end-to-end deep learning method, JointVesselNet, for robust extraction of 3D sparse vascular structure through embedding the image composition, generated by maximum intensity projection (MIP), into the 3D magnetic resonance angiography (MRA) volumetric image learning process to enhance the overall performance. The MIP embedding features can strengthen the local vessel signal and adapt to the geometric variability and scalability of vessels. Therefore, the proposed framework can better capture the small vessels and improve the vessel connectivity. To our knowledge, this is the first time that a deep learning framework is proposed to construct a joint convolutional embedding space, where the computed joint vessel probabilities from 2D projection and 3D volume can be integrated synergistically. Experimental results are evaluated and compared with the traditional 3D vessel segmentation methods and the state-of-the-art in deep learning, by using both public and real patient cerebrovascular image datasets.

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Vorheriges Kapitel ID-Fit: Intra-Saccular Device Adjustment for Personalized Cerebral Aneurysm Treatment
Nächstes Kapitel Classification of Retinal Vessels into Artery-Vein in OCT Angiography Guided by Fundus Images
Fußnoten
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Metadaten
Titel
JointVesselNet: Joint Volume-Projection Convolutional Embedding Networks for 3D Cerebrovascular Segmentation
verfasst von
Yifan Wang
Guoli Yan
Haikuan Zhu
Sagar Buch
Ying Wang
Ewart Mark Haacke
Jing Hua
Zichun Zhong
Copyright-Jahr
2020
Buch
Medical Image Computing and Computer Assisted Intervention – MICCAI 2020

Print ISBN: 978-3-030-59724-5

Electronic ISBN: 978-3-030-59725-2

Copyright-Jahr: 2020

DOI
https://doi.org/10.1007/978-3-030-59725-2_11

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