JointVesselNet: Joint Volume-Projection Convolutional Embedding Networks for 3D Cerebrovascular Segmentation
JointVesselNet: Joint Volume-Projection Convolutional Embedding Networks for 3D Cerebrovascular Segmentation
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DOI：
10.1007/978-3-030-59725-2_11
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发表时间：
2020-10
期刊：
影响因子：
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通讯作者：
Yifan Wang-;Guoli Yan;Haikuan Zhu;S. Buch;Ying Wang;E. Haacke;Jing Hua;Z. Zhong
中科院分区：
文献类型：
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作者：
Yifan Wang-;Guoli Yan;Haikuan Zhu;S. Buch;Ying Wang;E. Haacke;Jing Hua;Z. Zhong
在本文中，我们提出了一种端到端深度学习方法JointVesselNet，通过将最大强度投影（MIP）生成的图像组成嵌入到3D磁共振血管成像（MRA）体积图像学习过程中，来鲁棒提取3D稀疏血管结构，以提高整体性能。MIP嵌入特征可以增强局部船舶信号，适应船舶的几何可变性和可扩展性。因此，所提出的框架可以更好地捕获小型船舶，提高船舶连通性。据我们所知，这是第一次提出一个深度学习框架来构建联合卷积嵌入空间，在该空间中，从2D投影和3D体积计算的联合血管概率可以协同集成。通过使用公开的和真实的患者脑血管图像数据集，对实验结果与传统的3D血管分割方法和最新的深度学习方法进行了评估和比较。
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.