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Abstract #2665

Intracranial aneurysm segmentation using a deep convolutional neural network

Miaoqi Zhang1, Qingchu Jin2, Mingzhu Fu1, Hanyu Wei1, and Rui Li1
1Center for Biomedical Imaging Research, Department of Biomedical Engineering, Tsinghua University, Beijing, China, 2Johns Hopkins University, Baltimore, MD, United States

Intracranial aneurysms are abnormal dilations of the cerebral arteries that have a prevalence of 5-8% in the general population. In this study, we successfully segmented IAs from dual inputs (TOF-MRA and T1-VISTA) using the hyperdense net with higher accuracy than a single input. The maximum diameter measurements for IAs derived from our segmentation was consistent with the maximum diameters obtained from the criterion standard (DSA). We showed that larger aneurysms were easier to segment by the deep learning model. In the future, we will test other deep learning models on aneurysm segmentation and compare these results with the hyperdense net.

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