PlacentaNet: Automatic morphological characterization of placenta photos with deep learning

Yukun Chen*, Chenyan Wu, Zhuomin Zhang, Jeffery A. Goldstein, Alison D. Gernand, James Z. Wang

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contribution

2 Scopus citations

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.

Original languageEnglish (US)
Title of host publicationMedical Image Computing and Computer Assisted Intervention – MICCAI 2019 - 22nd International Conference, Proceedings
EditorsDinggang Shen, Pew-Thian Yap, Tianming Liu, Terry M. Peters, Ali Khan, Lawrence H. Staib, Caroline Essert, Sean Zhou
PublisherSpringer
Pages487-495
Number of pages9
ISBN (Print)9783030322380
DOIs
StatePublished - Jan 1 2019
Event22nd International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2019 - Shenzhen, China
Duration: Oct 13 2019Oct 17 2019

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume11764 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference22nd International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2019
CountryChina
CityShenzhen
Period10/13/1910/17/19

Keywords

  • Convolutional neural network
  • Placenta
  • Segmentation
  • Transfer learning

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)

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