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Real-time standard scan plane detection and localisation in fetal ultrasound using fully convolutional neural networks

Baumgartner CF, Kamnitsas K, Matthew J, Smith S, Kainz B, Rueckert D (2016)

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Publication Type: Conference contribution

Publication year: 2016

Journal

Publisher: Springer Verlag

Book Volume: 9901 LNCS

Pages Range: 203-211

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

ISBN: 9783319467221

DOI: 10.1007/978-3-319-46723-8_24

Abstract

Fetal mid-pregnancy scans are typically carried out according to fixed protocols. Accurate detection of abnormalities and correct biometric measurements hinge on the correct acquisition of clearly defined standard scan planes. Locating these standard planes requires a high level of expertise. However,there is a worldwide shortage of expert sonographers. In this paper,we consider a fully automated system based on convolutional neural networks which can detect twelve standard scan planes as defined by the UK fetal abnormality screening programme. The network design allows real-time inference and can be naturally extended to provide an approximate localisation of the fetal anatomy in the image. Such a framework can be used to automate or assist with scan plane selection,or for the retrospective retrieval of scan planes from recorded videos. The method is evaluated on a large database of 1003 volunteer mid-pregnancy scans. We show that standard planes acquired in a clinical scenario are robustly detected with a precision and recall of 69% and 80%,which is superior to the current state-of-the-art. Furthermore,we show that it can retrospectively retrieve correct scan planes with an accuracy of 71% for cardiac views and 81% for non-cardiac views.

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How to cite

APA:

Baumgartner, C.F., Kamnitsas, K., Matthew, J., Smith, S., Kainz, B., & Rueckert, D. (2016). Real-time standard scan plane detection and localisation in fetal ultrasound using fully convolutional neural networks. In Gozde Unal, Sebastian Ourselin, Leo Joskowicz, Mert R. Sabuncu, William Wells (Eds.), Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (pp. 203-211). Springer Verlag.

MLA:

Baumgartner, Christian F., et al. "Real-time standard scan plane detection and localisation in fetal ultrasound using fully convolutional neural networks." Proceedings of the Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) Ed. Gozde Unal, Sebastian Ourselin, Leo Joskowicz, Mert R. Sabuncu, William Wells, Springer Verlag, 2016. 203-211.

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