TY - GEN
T1 - PlacentaNet
T2 - 22nd International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2019
AU - Chen, Yukun
AU - Wu, Chenyan
AU - Zhang, Zhuomin
AU - Goldstein, Jeffery A.
AU - Gernand, Alison D.
AU - Wang, James Z.
N1 - Publisher Copyright:
© 2019, Springer Nature Switzerland AG.
PY - 2019
Y1 - 2019
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=85075629901&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85075629901&partnerID=8YFLogxK
U2 - 10.1007/978-3-030-32239-7_54
DO - 10.1007/978-3-030-32239-7_54
M3 - Conference contribution
AN - SCOPUS:85075629901
SN - 9783030322380
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 487
EP - 495
BT - Medical Image Computing and Computer Assisted Intervention – MICCAI 2019 - 22nd International Conference, Proceedings
A2 - Shen, Dinggang
A2 - Yap, Pew-Thian
A2 - Liu, Tianming
A2 - Peters, Terry M.
A2 - Khan, Ali
A2 - Staib, Lawrence H.
A2 - Essert, Caroline
A2 - Zhou, Sean
PB - Springer Science and Business Media Deutschland GmbH
Y2 - 13 October 2019 through 17 October 2019
ER -