Zhou Hanyue, Xiao Jiayu, Fan Zhaoyang, Ruan Dan
Department of Bioengineering, University of California, Los Angeles, CA 90095, US.
Biomedical Imaging Research Institute, Cedars-Sinai Medical Center, Los Angeles, CA 90048, US.
Proc IEEE Int Symp Biomed Imaging. 2021 Apr;2021:1416-1419. doi: 10.1109/ISBI48211.2021.9434018. Epub 2021 May 25.
Intracranial vessel wall segmentation is critical for the quantitative assessment of intracranial atherosclerosis based on magnetic resonance vessel wall imaging. This work further improves on a previous 2D deep learning segmentation network by the utilization of 1) a 2.5D structure to balance network complexity and regularizing geometry continuity; 2) a UNET++ model to achieve structure adaptation; 3) an additional approximated Hausdorff distance (HD) loss into the objective to enhance geometry conformality; and 4) landing in a commonly used morphological measure of plaque burden - the normalized wall index (NWI) - to match the clinical endpoint. The modified network achieved Dice similarity coefficient of 0.9172 ± 0.0598 and 0.7833 ± 0.0867, HD of 0.3252 ± 0.5071 mm and 0.4914 ± 0.5743 mm, mean surface distance of 0.0940 ± 0.0781 mm and 0.1408 ± 0.0917 mm for the lumen and vessel wall, respectively. These results compare favorably to those obtained by the original 2D UNET on all segmentation metrics. Additionally, the proposed segmentation network reduced the mean absolute error in NWI from 0.0732 ± 0.0294 to 0.0725 ± 0.0333.
基于磁共振血管壁成像对颅内动脉粥样硬化进行定量评估时，颅内血管壁分割至关重要。这项工作在先前的二维深度学习分割网络基础上进一步改进，具体方式如下：1）利用2.5D结构平衡网络复杂性并规范几何连续性；2）采用UNET++模型实现结构自适应；3）在目标函数中加入额外的近似豪斯多夫距离（HD）损失以增强几何共形性；4）引入常用的斑块负荷形态学测量指标——归一化管壁指数（NWI）——以匹配临床终点。改进后的网络在管腔和血管壁分割中，骰子相似系数分别达到0.9172±0.0598和0.7833±0.0867，HD分别为0.3252±0.5071毫米和0.4914±0.5743毫米，平均表面距离分别为0.0940±0.0781毫米和0.1408±0.0917毫米。在所有分割指标上，这些结果均优于原始二维UNET所获得的结果。此外，所提出的分割网络将NWI中的平均绝对误差从0.0732±0.0294降低至0.0725±0.0333。
