  Preterm neonates are highly likely to suffer from ventriculomegaly, a
dilation of the Cerebral Ventricular System (CVS). This condition can develop
into life-threatening hydrocephalus and is correlated with future
neuro-developmental impairments. Consequently, it must be detected and
monitored by physicians. In clinical routing, manual 2D measurements are
performed on 2D ultrasound (US) images to estimate the CVS volume but this
practice is imprecise due to the unavailability of 3D information. A way to
tackle this problem would be to develop automatic CVS segmentation algorithms
for 3D US data. In this paper, we investigate the potential of 2D and 3D
Convolutional Neural Networks (CNN) to solve this complex task and propose to
use Compositional Pattern Producing Network (CPPN) to enable the CNNs to learn
CVS location. Our database was composed of 25 3D US volumes collected on 21
preterm nenonates at the age of $35.8 \pm 1.6$ gestational weeks. We found that
the CPPN enables to encode CVS location, which increases the accuracy of the
CNNs when they have few layers. Accuracy of the 2D and 3D CNNs reached
intraobserver variability (IOV) in the case of dilated ventricles with Dice of
$0.893 \pm 0.008$ and $0.886 \pm 0.004$ respectively (IOV = $0.898 \pm 0.008$)
and with volume errors of $0.45 \pm 0.42$ cm$^3$ and $0.36 \pm 0.24$ cm$^3$
respectively (IOV = $0.41 \pm 0.05$ cm$^3$). 3D CNNs were more accurate than 2D
CNNs in the case of normal ventricles with Dice of $0.797 \pm 0.041$ against
$0.776 \pm 0.038$ (IOV = $0.816 \pm 0.009$) and volume errors of $0.35 \pm
0.29$ cm$^3$ against $0.35 \pm 0.24$ cm$^3$ (IOV = $0.2 \pm 0.11$ cm$^3$). The
best segmentation time of volumes of size $320 \times 320 \times 320$ was
obtained by a 2D CNN in $3.5 \pm 0.2$ s.
