  Convolutional neural networks (CNNs) have been widely and successfully used
for medical image segmentation. However, CNNs are typically considered to
require large numbers of dedicated expert-segmented training volumes, which may
be limiting in practice. This work investigates whether clinically obtained
segmentations which are readily available in picture archiving and
communication systems (PACS) could provide a possible source of data to train a
CNN for segmentation of organs-at-risk (OARs) in radiotherapy treatment
planning. In such data, delineations of structures deemed irrelevant to the
target clinical use may be lacking. To overcome this issue, we use multi-label
instead of multi-class segmentation. We empirically assess how many clinical
delineations would be sufficient to train a CNN for the segmentation of OARs
and find that increasing the training set size beyond a limited number of
images leads to sharply diminishing returns. Moreover, we find that by using
multi-label segmentation, missing structures in the reference standard do not
have a negative effect on overall segmentation accuracy. These results indicate
that segmentations obtained in a clinical workflow can be used to train an
accurate OAR segmentation model.
