Skip to main content
main-content

Tipp

Weitere Kapitel dieses Buchs durch Wischen aufrufen

2018 | OriginalPaper | Buchkapitel

3D Fetal Skull Reconstruction from 2DUS via Deep Conditional Generative Networks

verfasst von : Juan J. Cerrolaza, Yuanwei Li, Carlo Biffi, Alberto Gomez, Matthew Sinclair, Jacqueline Matthew, Caronline Knight, Bernhard Kainz, Daniel Rueckert

Erschienen in: Medical Image Computing and Computer Assisted Intervention – MICCAI 2018

Verlag: Springer International Publishing

share
TEILEN

Abstract

2D ultrasound (US) is the primary imaging modality in antenatal healthcare. Despite the limitations of traditional 2D biometrics to characterize the true 3D anatomy of the fetus, the adoption of 3DUS is still very limited. This is particularly significant in developing countries and remote areas, due to the lack of experienced sonographers and the limited access to 3D technology. In this paper, we present a new deep conditional generative network for the 3D reconstruction of the fetal skull from 2DUS standard planes of the head routinely acquired during the fetal screening process. Based on the generative properties of conditional variational autoencoders (CVAE), our reconstruction architecture (REC-CVAE) directly integrates the three US standard planes as conditional variables to generate a unified latent space of the skull. Additionally, we propose HiREC-CVAE, a hierarchical generative network based on the different clinical relevance of each predictive view. The hierarchical structure of HiREC-CVAE allows the network to learn a sequence of nested latent spaces, providing superior predictive capabilities even in the absence of some of the 2DUS scans. The performance of the proposed architectures was evaluated on a dataset of 72 cases, showing accurate reconstruction capabilities from standard non-registered 2DUS images.
Literatur
1.
Zurück zum Zitat Springett, A., et al.: Congenital Anomaly Statistics, 2012. England and Wales. Technical report, British Isles Network of Congenital Anomaly Registerss (2014) Springett, A., et al.: Congenital Anomaly Statistics, 2012. England and Wales. Technical report, British Isles Network of Congenital Anomaly Registerss (2014)
2.
Zurück zum Zitat Baumgartner, C., et al.: Sononet: real-time detection and localisation of fetal standard scan planes in freehand ultrasound. IEEE TMI 36, 2204–2215 (2017) Baumgartner, C., et al.: Sononet: real-time detection and localisation of fetal standard scan planes in freehand ultrasound. IEEE TMI 36, 2204–2215 (2017)
3.
Zurück zum Zitat Lima, J., et al.: Biometry and fetal weight estimation by two-dimensional and three-dimensional ultrasonography: an intraobserver and interobserver reliability and agreement study. Ultrasound Obstet. Gynecol. 40, 186–93 (2012) CrossRef Lima, J., et al.: Biometry and fetal weight estimation by two-dimensional and three-dimensional ultrasonography: an intraobserver and interobserver reliability and agreement study. Ultrasound Obstet. Gynecol. 40, 186–93 (2012) CrossRef
4.
Zurück zum Zitat Matthew, J., et al. Novel 3D ultrasound-based metric to assess the fetal skull: a pilot study. In: BMUS Annual Meeting (2017) Matthew, J., et al. Novel 3D ultrasound-based metric to assess the fetal skull: a pilot study. In: BMUS Annual Meeting (2017)
5.
Zurück zum Zitat Lee, S., et al.: Prenatal three-dimensional ultrasound: perception of sonographers, sonologists and undergraduate students. Ultrasound Obstet. Gynecol. 1(30), 77–80 (2007) CrossRef Lee, S., et al.: Prenatal three-dimensional ultrasound: perception of sonographers, sonologists and undergraduate students. Ultrasound Obstet. Gynecol. 1(30), 77–80 (2007) CrossRef
7.
Zurück zum Zitat Shah, S., et al.: Perceived barriers in the use of ultrasound in developing countries. Crit. Ultrasound J. 7(1), 7–11 (2015) CrossRef Shah, S., et al.: Perceived barriers in the use of ultrasound in developing countries. Crit. Ultrasound J. 7(1), 7–11 (2015) CrossRef
8.
Zurück zum Zitat Whitmarsh, T., et al.: Reconstructing the 3D shape and bone mineral density distribution of the proximal femur from dual-energy x-ray absorptiometry. IEEE TMI 12(30), 2101–2114 (2011) Whitmarsh, T., et al.: Reconstructing the 3D shape and bone mineral density distribution of the proximal femur from dual-energy x-ray absorptiometry. IEEE TMI 12(30), 2101–2114 (2011)
9.
Zurück zum Zitat Ehlke, M., et al.: Fast generation of virtual x-ray images for reconstruction of 3D anatomy. IEEE TVCG 19(12), 2673–2682 (2013) Ehlke, M., et al.: Fast generation of virtual x-ray images for reconstruction of 3D anatomy. IEEE TVCG 19(12), 2673–2682 (2013)
11.
Zurück zum Zitat Wang, L., Fang, Y.: Unsupervised 3D reconstruction from a single image via adversarial learning (2017). CoRR, abs/1711.09312 Wang, L., Fang, Y.: Unsupervised 3D reconstruction from a single image via adversarial learning (2017). CoRR, abs/1711.09312
13.
Zurück zum Zitat Kingma, D.P., Welling, M.: Auto-encoding variational bayes. In: ICLR 2014 (2014) Kingma, D.P., Welling, M.: Auto-encoding variational bayes. In: ICLR 2014 (2014)
Metadaten
Titel
3D Fetal Skull Reconstruction from 2DUS via Deep Conditional Generative Networks
verfasst von
Juan J. Cerrolaza
Yuanwei Li
Carlo Biffi
Alberto Gomez
Matthew Sinclair
Jacqueline Matthew
Caronline Knight
Bernhard Kainz
Daniel Rueckert
Copyright-Jahr
2018
DOI
https://doi.org/10.1007/978-3-030-00928-1_44

Premium Partner