We propose a method to extract the human placenta at late gestation using multi-view 3D US images. This is the first step towards automatic quantification of placental volume and morphology from US images along the whole pregnancy beyond early stages (where the entire placenta can be captured with a single 3D US image). Our method uses 3D US images from different views acquired with a multi-probe system. A whole placenta segmentation is obtained from these images by using a novel technique based on 3D convolutional neural networks. We demonstrate the performance of our method on 3D US images of the placenta in the last trimester. We achieve a high Dice overlap of up to 0.8 with respect to manual annotations, and the derived placental volumes are comparable to corresponding volumes extracted from MR.
Original languageEnglish
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
Number of pages9
Publication statusE-pub ahead of print - 10 Oct 2019

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume11768 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


Dive into the research topics of 'Towards whole placenta segmentation at late gestation using multi-view ultrasound images'. Together they form a unique fingerprint.

Cite this