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.