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 3.83mm/12.7 degrees and
3.80mm/12.6 degrees for the transventricular and transcerebellar planes
respectively and takes 0.46s per plane.