  Deep Neural Networks (DNNs) have the potential for making various clinical
procedures more time-efficient by automating medical image segmentation. Due to
their strong, in some cases human-level, performance, they have become the
standard approach in this field. The design of the best possible medical image
segmentation DNNs, however, is task-specific. Neural Architecture Search (NAS),
i.e., the automation of neural network design, has been shown to have the
capability to outperform manually designed networks for various tasks. However,
the existing NAS methods for medical image segmentation have explored a quite
limited range of types of DNN architectures that can be discovered. In this
work, we propose a novel NAS search space for medical image segmentation
networks. This search space combines the strength of a generalised
encoder-decoder structure, well known from U-Net, with network blocks that have
proven to have a strong performance in image classification tasks. The search
is performed by looking for the best topology of multiple cells simultaneously
with the configuration of each cell within, allowing for interactions between
topology and cell-level attributes. From experiments on two publicly available
datasets, we find that the networks discovered by our proposed NAS method have
better performance than well-known handcrafted segmentation networks, and
outperform networks found with other NAS approaches that perform only topology
search, and topology-level search followed by cell-level search.
