  This paper describes our method for our participation in the FeTA
challenge2021 (team name: TRABIT). The performance of convolutional neural
networks for medical image segmentation is thought to correlate positively with
the number of training data. The FeTA challenge does not restrict participants
to using only the provided training data but also allows for using other
publicly available sources. Yet, open access fetal brain data remains limited.
An advantageous strategy could thus be to expand the training data to cover
broader perinatal brain imaging sources. Perinatal brain MRIs, other than the
FeTA challenge data, that are currently publicly available, span normal and
pathological fetal atlases as well as neonatal scans. However, perinatal brain
MRIs segmented in different datasets typically come with different annotation
protocols. This makes it challenging to combine those datasets to train a deep
neural network. We recently proposed a family of loss functions, the label-set
loss functions, for partially supervised learning. Label-set loss functions
allow to train deep neural networks with partially segmented images, i.e.
segmentations in which some classes may be grouped into super-classes. We
propose to use label-set loss functions to improve the segmentation performance
of a state-of-the-art deep learning pipeline for multi-class fetal brain
segmentation by merging several publicly available datasets. To promote
generalisability, our approach does not introduce any additional
hyper-parameters tuning.
