Paper: 3D intracranial artery segmentation using a convolutional autoencoder


3D intracranial artery segmentation using a convolutional autoencoder

Li Chen1, Yanjun Xie2, Jie Sun3, Niranjan Balu3, Mahmud Mossa-Basha3, Kristi Pimentel3, Thomas S. Hatsukami4, Jenq-Neng Hwang1, Chun Yuan3†

Department of 1. Electrical Engineering; 2. Mechanical Engineering; 3. Radiology; 4. Surgery

University of Washington

Seattle, WA, 98195, USA

{cluw, yanjunx. sunjie, ninja, mmossab, kristidb, tomhat, hwang, cyuan }@uw.edu

Corresponding author


Abstract— Automated segmentation of intracranial arteries on magnetic resonance angiography (MRA) allows for quantification of cerebrovascular features, which provides tools for understanding aging and pathophysiological adaptations of the cerebrovascular system. Using a convolutional autoencoder (CAE) for segmentation is promising as it takes advantage of the autoencoder structure in effective noise reduction and feature extraction by representing high dimensional information with low dimensional latent variables. In this paper, we trained an 8-layer CAE to learn a 3D segmentation model of intracranial arteries from 49 cases of MRA data. After parameter optimization and prediction refinement, our trained model was shown to perform better than the three traditional segmentation methods in both binary classification and visual evaluation.

Keywords- convolutional autoencoder; deep neural network; machine learning; artery segmentation; Magnetic Resonance Angiography


The IEEE International Conference on Bioinformatics and Biomedicine (BIBM) 2017

http://ieeexplore.ieee.org/document/8217741/

B245.pdf

IEEE BIBM 2017

https://muii.missouri.edu/bibm2017/


Improved version: Y-net

published on arxiv

https://arxiv.org/abs/1712.07194

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