INTRACRANIAL VESSEL WALL SEGMENTATION FOR ATHEROSCLEROTIC PLAQUE QUANTIFICATION.
INTRACRANIAL VESSEL WALL SEGMENTATION FOR ATHEROSCLEROTIC PLAQUE QUANTIFICATION.
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DOI：
10.1109/isbi48211.2021.9434018
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发表时间：
2021-04
期刊：
影响因子：
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通讯作者：
Ruan D
中科院分区：
文献类型：
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作者：
Zhou H;Xiao J;Fan Z;Ruan D
颅内血管壁分割是基于磁共振血管壁成像定量评估颅内动脉粥样硬化的关键。这项工作进一步改进了以前的2D深度学习分割网络，利用1）2.5D结构来平衡网络复杂性和正则化几何连续性; 2）UNET++模型来实现结构自适应; 3）额外的近似Hausdorff距离（HD）损失到目标中以增强几何一致性;和4）采用常用的斑块负荷形态学指标-标准化壁指数（NWI）-以匹配临床终点。改进后的网络获得的Dice相似系数分别为0.9172 ± 0.0598和0.7833 ± 0.0867，HD分别为0.3252 ± 0.5071 mm和0.4914 ± 0.5743 mm，管腔和血管壁的平均表面距离分别为0.0940 ± 0.0781 mm和0.1408 ± 0.0917 mm。这些结果与原始2D UNET在所有分割指标上获得的结果相比毫不逊色。此外，提出的分割网络将NWI的平均绝对误差从0.0732 ± 0.0294降低到0.0725 ± 0.0333。
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.