PlacentaNet: Automatic Morphological Characterization of Placenta Photos with Deep Learning
Yukun Chen (1), Chenyan Wu (1), Zhuomin Zhang (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:
Analysis of the placenta is extremely useful for evaluating health
risks of the mother and baby after delivery. In this paper, we tackle
the problem of automatic morphological characterization of placentas,
including the tasks of placenta image segmentation, umbilical cord
insertion point localization, and maternal/fetal side
classification. We curated an existing dataset consisting of around
1,000 placenta images taken at Northwestern Memorial Hospital,
together with their pixel-level segmentation map. We propose a novel
pipeline, PlacentaNet, which consists of three encoder-decoder
convolutional neural networks with a shared encoder, to address these
morphological characterization tasks by employing a transfer learning
training strategy. We evaluated its effectiveness using the curated
dataset as well as the pathology reports in the medical record. The
system produced accurate morphological characterization, which enabled
subsequent feature analysis of placentas. In particular, we show
promising results for detection of retained placenta (i.e., incomplete
placenta) and umbilical cord insertion type categorization, both of
which may possess clinical impact.
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Citation:
Yukun Chen, Chenyan Wu, Zhuomin Zhang, Jeffery A. Goldstein, Alison
D. Gernand and James Z. Wang, ``PlacentaNet: Automatic Morphological Characterization of Placenta Photos with Deep Learning,'' Proceedings of the
International Conference on Medical Image Computing and Computer
Assisted Interventions, pp. 487-495, Shenzhen, China, October 2019.
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November 25, 2019
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