Abstract #0455

Intracranial Vessel Wall Segmentation with 2.5D UNet++ Deep Learning Network

Hanyue Zhou¹, Jiayu Xiao², Debiao Li^1,2, Dan Ruan^1,3, and Zhaoyang Fan^2,4,5

¹Bioengineering, University of California, Los Angeles, Los Angeles, CA, United States, ²Cedars-Sinai Medical Center, Los Angeles, CA, United States, ³Radiation Oncology, University of California, Los Angeles, Los Angeles, CA, United States, ⁴Radiology, University of Southern California, Los Angeles, CA, United States, ⁵Radiation Oncology, University of Southern California, Los Angeles, CA, United States

Intracranial vessel wall segmentation is an essential step for the intracranial atherosclerosis quantification. We have developed an automated intracranial vessel wall segmentation method based on deep learning that utilized a 2.5D UNet++ network structure with a loss function consists of both soft Dice coefficient loss and the approximated Hausdorff distance loss. We show that we have achieved significant improvements over our previous segmentation model based on a 2D UNet structure across various quantitative measures, as well as a better visual resemblance to the ground truth segmentation.

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