Fast fully automatic segmentation of the human placenta from motion corrupted MRI

Amir Alansary*, Konstantinos Kamnitsas, Alice Davidson, Rostislav Khlebnikov, Martin Rajchl, Christina Malamateniou, Mary Rutherford, Joseph V. Hajnal, Ben Glocker, Daniel Rueckert, Bernhard Kainz

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference paperpeer-review

59 Citations (Scopus)


Recently,magnetic resonance imaging has revealed to be important for the evaluation of placenta’s health during pregnancy. Quantitative assessment of the placenta requires a segmentation,which proves to be challenging because of the high variability of its position,orientation,shape and appearance. Moreover,image acquisition is corrupted by motion artifacts from both fetal and maternal movements. In this paper we propose a fully automatic segmentation framework of the placenta from structural T2-weighted scans of the whole uterus,as well as an extension in order to provide an intuitive pre-natal view into this vital organ. We adopt a 3D multi-scale convolutional neural network to automatically identify placental candidate pixels. The resulting classification is subsequently refined by a 3D dense conditional random field,so that a high resolution placental volume can be reconstructed from multiple overlapping stacks of slices. Our segmentation framework has been tested on 66 subjects at gestational ages 20–38 weeks achieving a Dice score of 71.95 ± 19.79% for healthy fetuses with a fixed scan sequence and 66.89 ± 15.35% for a cohort mixed with cases of intrauterine fetal growth restriction using varying scan parameters.

Original languageEnglish
Title of host publicationMedical Image Computing and Computer-Assisted Intervention – MICCAI 2016
Number of pages9
VolumeLNCS 9901
ISBN (Electronic)978-3-319-46723-8
Publication statusE-pub ahead of print - 2 Oct 2016

Publication series

NameLecture Notes in Computer Science
PublisherSpringer Verlag
ISSN (Print)0302-9743


  • Fetal
  • MRI
  • Placenta
  • Segmentation


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