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

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.