TY - JOUR
T1 - AI-PLAX
T2 - AI-based placental assessment and examination using photos
AU - Chen, Yukun
AU - Zhang, Zhuomin
AU - Wu, Chenyan
AU - Davaasuren, Dolzodmaa
AU - Goldstein, Jeffery A.
AU - Gernand, Alison D.
AU - Wang, James Z.
N1 - Funding Information:
This work was supported primarily by the B ill & Melinda Gates Foundation under grant no. OPP1195074. The research was also supported by a grant from the College of Information Sciences and Technology at The Pennsylvania State University. The computation was supported by the NVIDIA Corporations GPU Grant Program and the Extreme Science and Engineering Discovery Environment (XSEDE), which is supported by National Science Foundation grant number ACI-1548562. Throughout this work, Dr. William (Tony) Parks has provided helpful, expert input as a perinatal pathologist. Celeste Beck and Leigh A. Taylor assisted in dataset curation and are part of the larger research team.
Funding Information:
This work was supported primarily by the Bill & Melinda Gates Foundation under grant no. OPP1195074. The research was also supported by a grant from the College of Information Sciences and Technology at The Pennsylvania State University. The computation was supported by the NVIDIA Corporations GPU Grant Program and the Extreme Science and Engineering Discovery Environment (XSEDE), which is supported by National Science Foundation grant number ACI-1548562. Throughout this work, Dr. William (Tony) Parks has provided helpful, expert input as a perinatal pathologist. Celeste Beck and Leigh A. Taylor assisted in dataset curation and are part of the larger research team.
Publisher Copyright:
© 2020 The Authors
PY - 2020/9
Y1 - 2020/9
N2 - 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.
AB - 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.
KW - Deep learning
KW - Pathology
KW - Photo image analysis
KW - Placenta
KW - Transfer learning
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U2 - 10.1016/j.compmedimag.2020.101744
DO - 10.1016/j.compmedimag.2020.101744
M3 - Article
C2 - 32634729
AN - SCOPUS:85087332720
VL - 84
JO - Computerized Medical Imaging and Graphics
JF - Computerized Medical Imaging and Graphics
SN - 0895-6111
M1 - 101744
ER -