  Ventricular volume and its progression are known to be linked to several
brain diseases such as dementia and schizophrenia. Therefore accurate
measurement of ventricle volume is vital for longitudinal studies on these
disorders, making automated ventricle segmentation algorithms desirable. In the
past few years, deep neural networks have shown to outperform the classical
models in many imaging domains. However, the success of deep networks is
dependent on manually labeled data sets, which are expensive to acquire
especially for higher dimensional data in the medical domain. In this work, we
show that deep neural networks can be trained on much-cheaper-to-acquire
pseudo-labels (e.g., generated by other automated less accurate methods) and
still produce more accurate segmentations compared to the quality of the
labels. To show this, we use noisy segmentation labels generated by a
conventional region growing algorithm to train a deep network for lateral
ventricle segmentation. Then on a large manually annotated test set, we show
that the network significantly outperforms the conventional region growing
algorithm which was used to produce the training labels for the network. Our
experiments report a Dice Similarity Coefficient (DSC) of $0.874$ for the
trained network compared to $0.754$ for the conventional region growing
algorithm ($p < 0.001$).
