Convolutional Neural Network with Asymmetric Encoding and Decoding Structure for Brain Vessel Segmentation on Computed Tomographic Angiography.

Segmenting 3D brain vessels on computed tomographic angiography is critical for early diagnosis of stroke. However, traditional filter and optimization-based methods are ineffective in this challenging task due to imaging quality limits and structural complexity. And learning based methods are difficult to be used in this task due to extremely high time consumption in manually labeling and the lack of labelled open datasets. To address this, in this paper, we develop an asymmetric encoding and decoding-based convolutional neural network for accurate vessel segmentation on computed tomographic angiography. In the network, 3D encoding module is designed to comprehensively extract 3D vascular structure information. And three 2D decoding modules are designed to optimally identify vessels on each 2D plane, so that the network can learn more complex vascular structures, and has stronger ability to distinguish vessels from normal regions. What is more, to improve insufficient fine vessel segmentation caused by pixel-wise loss function, we develop a centerline loss to guide learning model to pay equal attention to small vessels and large vessels, so that the segmentation accuracy of small vessels can be improved. Compared to two state-of-the-art approaches, our model achieved superior performance, demonstrating the effectiveness of both whole vessel segmentation and small vessel maintenance.