Decidual Vasculopathy Identification in Whole Slide Images Using Multiresolution Hierarchical Convolutional Neural Networks
Decidual Vasculopathy Identification in Whole Slide Images Using Multiresolution Hierarchical Convolutional Neural Networks
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DOI：
10.1016/j.ajpath.2020.06.014
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发表时间：
2020-10-01
影响因子：
6
通讯作者：
LeDuc, Philip
中科院分区：
文献类型：
--
作者：
Clymer, Daniel;Kostadinov, Stefan;LeDuc, Philip
婴儿出生后，病理学家检查胎盘是否存在异常，如感染或母体血管灌注不良，可以提供有关婴儿即时和长期健康的重要信息。病理性胎盘血管病变蜕膜血管病变（DV）的检测已被证明可以预测不良妊娠结局，如先兆子痫，这可能导致母亲和新生儿在随后的怀孕发病率。然而，由于大医院的分娩量大，资源有限，目前有很大一部分分娩的胎盘未经检查就被丢弃。此外，DV的正确诊断通常需要经验丰富的围产期病理学家的专业知识。我们引入了一种分层机器学习方法，用于数字化胎盘载玻片中DV病变的自动检测和分类，沿着一种将学习到的图像特征与患者元数据相结合的方法，以预测DV的存在。最终，该方法将允许以更标准化的方式筛选更多的胎盘，提供关于哪些病例将从更深入的病理检查中受益最多的反馈。人类胎盘的这种计算机辅助检查将能够实时调整婴儿和孕产妇护理以及可能的化学预防（例如阿司匹林治疗），以预防先兆子痫，这是一种影响全球2%至8%的妊娠的疾病，被确定为未来妊娠的风险。
After a child is born, the examination of the placenta by a pathologist for abnormalities, such as infection or maternal vascular malperfusion, can provide important information about the immediate and long-term health of the infant. Detection of the pathologic placental blood vessel lesion decidual vasculopathy (DV) has been shown to predict adverse pregnancy outcomes, such as preeclampsia, which can lead to mother and neonatal morbidity in subsequent pregnancies. However, because of the high volume of deliveries at large hospitals and limited resources, currently a large proportion of delivered placentas are discarded without inspection. Furthermore, the correct diagnosis of DV often requires the expertise of an experienced perinatal pathologist. We introduce a hierarchical machine learning approach for the automated detection and classification of DV lesions in digitized placenta slides, along with a method of coupling learned image features with patient metadata to predict the presence of DV. Ultimately, the approach will allow many more placentas to be screened in a more standardized manner, providing feedback about which cases would benefit most from more in-depth pathologic inspection. Such computer-assisted examination of human placentas will enable real-time adjustment to infant and maternal care and possible chemoprevention (eg, aspirin therapy) to prevent preeclampsia, a disease that affects 2% to 8% of pregnancies worldwide, in women identified to be at risk with future pregnancies.