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Standard plane detection in 3D fetal ultrasound using an iterative transformation network

Li Y, Khanal B, Hou B, Alansary A, Cerrolaza JJ, Sinclair M, Matthew J, Gupta C, Knight C, Kainz B, Rueckert D (2018)

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Publication Type: Conference contribution

Publication year: 2018

Journal

Publisher: Springer Verlag

Book Volume: 11070 LNCS

Pages Range: 392-400

Conference Proceedings Title: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

Event location: Granada, ESP

ISBN: 9783030009274

DOI: 10.1007/978-3-030-00928-1_45

Abstract

Standard scan plane detection in fetal brain ultrasound (US) forms a crucial step in the assessment of fetal development. In clinical settings, this is done by manually manoeuvring a 2D probe to the desired scan plane. With the advent of 3D US, the entire fetal brain volume containing these standard planes can be easily acquired. However, manual standard plane identification in 3D volume is labour-intensive and requires expert knowledge of fetal anatomy. We propose a new Iterative Transformation Network (ITN) for the automatic detection of standard planes in 3D volumes. ITN uses a convolutional neural network to learn the relationship between a 2D plane image and the transformation parameters required to move that plane towards the location/orientation of the standard plane in the 3D volume. During inference, the current plane image is passed iteratively to the network until it converges to the standard plane location. We explore the effect of using different transformation representations as regression outputs of ITN. Under a multi-task learning framework, we introduce additional classification probability outputs to the network to act as confidence measures for the regressed transformation parameters in order to further improve the localisation accuracy. When evaluated on 72 US volumes of fetal brain, our method achieves an error of 3.83 mm/12.7° and 3.80 mm/12.6° for the transventricular and transcerebellar planes respectively and takes 0.46 s per plane.

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How to cite

APA:

Li, Y., Khanal, B., Hou, B., Alansary, A., Cerrolaza, J.J., Sinclair, M.,... Rueckert, D. (2018). Standard plane detection in 3D fetal ultrasound using an iterative transformation network. In Julia A. Schnabel, Christos Davatzikos, Carlos Alberola-López, Gabor Fichtinger, Alejandro F. Frangi (Eds.), Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (pp. 392-400). Granada, ESP: Springer Verlag.

MLA:

Li, Yuanwei, et al. "Standard plane detection in 3D fetal ultrasound using an iterative transformation network." Proceedings of the 21st International Conference on Medical Image Computing and Computer Assisted Intervention, MICCAI 2018, Granada, ESP Ed. Julia A. Schnabel, Christos Davatzikos, Carlos Alberola-López, Gabor Fichtinger, Alejandro F. Frangi, Springer Verlag, 2018. 392-400.

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