Fetal Skull Reconstruction via Deep Convolutional Autoencoders

Cerrolaza JJ, Li Y, Biffi C, Gomez A, Matthew J, Sinclair M, Gupta C, Knight CL, Rueckert D (2018)

Publication Type: Conference contribution

Publication year: 2018

Journal

Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society IEEE Engineering in Medicine and Biology Society

Publisher: Institute of Electrical and Electronics Engineers Inc.

Book Volume: 2018-July

Pages Range: 887-890

Conference Proceedings Title: Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS

Event location: Honolulu, HI, USA

ISBN: 9781538636466

DOI: 10.1109/EMBC.2018.8512282

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.

Involved external institutions

Imperial College London / The Imperial College of Science, Technology and Medicine GB United Kingdom (GB) King’s College London GB United Kingdom (GB)

How to cite

APA:

Cerrolaza, J.J., Li, Y., Biffi, C., Gomez, A., Matthew, J., Sinclair, M.,... Rueckert, D. (2018). Fetal Skull Reconstruction via Deep Convolutional Autoencoders. In Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS (pp. 887-890). Honolulu, HI, USA: Institute of Electrical and Electronics Engineers Inc..

MLA:

Cerrolaza, Juan J., et al. "Fetal Skull Reconstruction via Deep Convolutional Autoencoders." Proceedings of the 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2018, Honolulu, HI, USA Institute of Electrical and Electronics Engineers Inc., 2018. 887-890.

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