The Pennsylvania State University, University Park, PN, USA.
The Pennsylvania State University, University Park, PN, USA.
Comput Med Imaging Graph. 2020 Sep;84:101744. doi: 10.1016/j.compmedimag.2020.101744. Epub 2020 Jun 1.
Post-delivery analysis of the placenta is useful for evaluating health risks of both the mother and baby. In the U.S., however, only about 20% of placentas are assessed by pathology exams, and placental data is often missed in pregnancy research because of the additional time, cost, and expertise needed. A computer-based tool that can be used in any delivery setting at the time of birth to provide an immediate and comprehensive placental assessment would have the potential to not only to improve health care, but also to radically improve medical knowledge. In this paper, we tackle the problem of automatic placental assessment and examination using photos. More concretely, we first address morphological characterization, which includes the tasks of placental image segmentation, umbilical cord insertion point localization, and maternal/fetal side classification. We also tackle clinically meaningful feature analysis of placentas, which comprises detection of retained placenta (i.e., incomplete placenta), umbilical cord knot, meconium, abruption, chorioamnionitis, and hypercoiled cord, and categorization of umbilical cord insertion type. We curated a dataset consisting of approximately 1300 placenta images taken at Northwestern Memorial Hospital, with hand-labeled pixel-level segmentation map, cord insertion point and other information extracted from the associated pathology reports. We developed the AI-based Placental Assessment and Examination system (AI-PLAX), which is a novel two-stage photograph-based pipeline for fully automated analysis. In the first stage, we use three encoder-decoder convolutional neural networks with a shared encoder to address morphological characterization tasks by employing a transfer-learning training strategy. In the second stage, we employ distinct sub-models to solve different feature analysis tasks by using both the photograph and the output of the first stage. We evaluated the effectiveness of our pipeline by using the curated dataset as well as the pathology reports in the medical record. Through extensive experiments, we demonstrate our system is able to produce accurate morphological characterization and very promising performance on aforementioned feature analysis tasks, all of which may possess clinical impact and contribute to future pregnancy research. This work is the first for comprehensive, automated, computer-based placental analysis and will serve as a launchpad for potentially multiple future innovations.
胎盘娩出后的分析对于评估母婴的健康风险非常有用。然而，在美国，只有大约 20%的胎盘通过病理检查进行评估，由于需要额外的时间、成本和专业知识，胎盘数据在妊娠研究中经常被遗漏。如果有一种基于计算机的工具可以在分娩时在任何分娩环境中使用，以便立即提供全面的胎盘评估，那么它不仅有可能改善医疗保健，而且还有可能彻底改善医学知识。在本文中，我们使用照片解决胎盘自动评估和检查的问题。更具体地说，我们首先解决形态学特征描述问题，其中包括胎盘图像分割、脐带插入点定位和母体/胎儿侧分类任务。我们还解决了胎盘的临床有意义的特征分析问题，其中包括检测残留胎盘（即胎盘不完全）、脐带结、胎粪、胎盘早剥、绒毛膜羊膜炎和脐带缠绕，以及脐带插入类型的分类。我们整理了一个大约包含 1300 张在西北纪念医院拍摄的胎盘图像的数据集，这些图像都带有手标注的像素级分割图，以及从相关病理报告中提取的脐带插入点和其他信息。我们开发了基于人工智能的胎盘评估和检查系统（AI-PLAX），这是一种新颖的基于两阶段照片的流水线，用于全自动分析。在第一阶段，我们使用三个具有共享编码器的编码器-解码器卷积神经网络，通过采用迁移学习训练策略来解决形态学特征描述任务。在第二阶段，我们使用不同的子模型，通过使用照片和第一阶段的输出来解决不同的特征分析任务。我们使用整理好的数据集和病历中的病理报告来评估我们的管道的有效性。通过广泛的实验，我们证明了我们的系统能够产生准确的形态学特征描述，并在上述特征分析任务中表现出非常有前景的性能，所有这些都可能具有临床影响，并为未来的妊娠研究做出贡献。这项工作是全面、自动、基于计算机的胎盘分析的首次尝试，将成为未来多项创新的起点。
