Medical Image Computing and Computer Assisted Intervention - MICCAI 2020 - 23rd International Conference, Lima, Peru, October 4-8, 2020, Proceedings, Part II
Medical Image Computing and Computer Assisted Intervention - MICCAI 2020 - 23rd International Conference, Lima, Peru, October 4-8, 2020, Proceedings, Part II
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医学图像计算和计算机辅助干预 - MICCAI 2020 - 第 23 届国际会议，秘鲁利马，2020 年 10 月 4-8 日，会议记录，第二部分
DOI：
10.1007/978-3-030-59713-9_25
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发表时间：
2020
期刊：
影响因子：
--
通讯作者：
Uthoff J
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文献类型：
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作者：
Uthoff J
心脏磁共振成像（CMRI）提供心脏和周围组织的无创特征。它是肺动脉高压（PAH）预后的重要工具，PAH是一种具有异质表现的疾病，使生存可能性预测成为一项具有挑战性的任务。在本文中，我们提出了ageodesicallysoothedtensor特征学习方法（GST），该方法不仅利用心脏，还利用其周围组织来表征疾病的严重程度，以改善预后。具体来说，GST包括由测地线环围绕的心脏结构，这些测地线环用高斯滤波器逐渐平滑。这为随后的基于张量的特征学习管道调制患者位置差异提供了附加的洞察力。我们对150例确诊的PAH患者进行了四室和短轴CMRI评估，并进行了1年死亡率普查（27例死亡，123例存活）。与四室方案（AUC: 0.77; Cox4YD: 0.35）的右心室收缩末期容积指数（RVESVi: AUC: 0.58; Cox4YD: 0.18）的标准化测量相比，所提出的GST方法改善了成像后4年的AUC和Cox差异（Cox4YD）。在短轴扫描中，只有AUC比RVESVi有所改善（AUC: 0.77; Cox4YD: 0.16）。
Cardiac magnetic resonance imaging (CMRI) provides non-invasive characterization of the heart and surrounding tissues. It is an important tool for the prognosis of pulmonary arterial hypertension (PAH), a disease with heterogeneous presentation that makes survival likelihood prediction a challenging task. In this paper, we propose aGeodesicallySmooothedTensor feature learning method (GST) that utilizes not only the heart but also its surrounding tissues to characterize disease severity for improving prognosis. Specifically, GST includes structures surrounding the heart by geodesic rings which were incrementally smoothed with Gaussian filters. This provides additive insight while modulating for patient positional differences for a subsequent tensor-based feature learning pipeline. We performed evaluation on Four Chamber and Short Axis CMRI from 150 individuals with confirmed PAH and 1-year mortality census (27 deceased, 123 alive). The proposed GST method improved AUC and Cox difference at 4-years post-imaging (Cox4YD) over the standardized measurement of right ventricular end systolic volume index (RVESVi: AUC: 0.58; Cox4YD: 0.18) on the Four Chamber protocol (AUC: 0.77; Cox4YD: 0.35). Only AUC was improved over RVESVi in the Short Axis scans (AUC: 0.77; Cox4YD: 0.16).