AI-PLAX: AI-based Placental Assessment and Examination Using Photos
Yukun Chen (1), Zhuomin Zhang (1), Chenyan Wu (1), Dolzodmaa Davaasuren (1),
Jeffery A. Goldstein (2), Alison D. Gernand (1), and James Z. Wang (1)
(1) The Pennsylvania State University, University Park, Pennsylvania, USA
(2) Northwestern Memorial Hospital, Chicago, Illinois, USA
Abstract:
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 analysisof 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 1,300 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.
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Citation:
Yukun Chen, Zhuomin Zhang, Chenyan Wu, Dolzodmaa Davaasuren, Jeffery
A. Goldstein, Alison D. Gernand and James Z. Wang, ``AI-PLAX: AI-based
Placental Assessment and Examination using Photos,'' Computerized
Medical Imaging and Graphics, vol. 84, article no. 101744, pp. 1-15, 2020.
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Last Modified:
May 29, 2020
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