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Fetal Skull Reconstruction via Deep Convolutional Autoencoders

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

Juan J. Cerrolaza, Yuanwei Li, Carlo Biffi, Alberto Gomez, Jaqueline Matthew, Matthew Sinclair, Chandni Gupta, Caroline L. Knight, Daniel Rueckert

Original languageEnglish
Title of host publication40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2018
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages887-890
Number of pages4
Volume2018-July
ISBN (Electronic)9781538636466
DOIs
Published26 Oct 2018
Event40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2018 - Honolulu, United States
Duration: 18 Jul 201821 Jul 2018

Conference

Conference40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2018
Country/TerritoryUnited States
CityHonolulu
Period18/07/201821/07/2018

King's Authors

Abstract

Ultrasound (US) imaging is arguably the most commonly used modality for fetal screening. Recently, 3DUS has been progressively adopted in modern obstetric practice, showing promising diagnosis capabilities, and alleviating many of the inherent limitations of traditional 2DUS, such as subjectivity and operator dependence. However, the involuntary movements of the fetus, and the difficulty for the operator to inspect the entire volume in real-time can hinder the acquisition of the entire region of interest. In this paper, we present two deep convolutional architectures for the reconstruction of the fetal skull in partially occluded 3DUS volumes: a TL deep convolutional network (TL-Net), and a conditional variational autoencoder (CVAE). The performance of the two networks was evaluated for occlusion rates up to 50%, both showing accurate results even when only 60% of the skull is included in the US volume (Dice coeff. 0.84\pm 0.04 for CVAE and 0.83\pm 0.03 for TL-Net). The reconstruction networks proposed here have the potential to optimize image acquisition protocols in obstetric sonography, reducing the acquisition time and providing comprehensive anatomical information even from partially occluded images.

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