  Deep learning has made a remarkable impact in the field of natural image
processing over the past decade. Consequently, there is a great deal of
interest in replicating this success across unsolved tasks in related domains,
such as medical image analysis. Core to medical image analysis is the task of
semantic segmentation which enables various clinical workflows. Due to the
challenges inherent in manual segmentation, many decades of research have been
devoted to discovering extensible, automated, expert-level segmentation
techniques. Given the groundbreaking performance demonstrated by recent neural
network-based techniques, deep learning seems poised to achieve what classic
methods have historically been unable.
  This paper will briefly overview some of the state-of-the-art (SoTA) neural
network-based segmentation algorithms with a particular emphasis on the most
recent architectures, comparing and contrasting the contributions and
characteristics of each network topology. Using ultrasonography as a motivating
example, it will also demonstrate important clinical implications of effective
deep learning-based solutions, articulate challenges unique to the modality,
and discuss novel approaches developed in response to those challenges,
concluding with the proposal of future directions in the field.
  Given the generally observed ephemerality of the best deep learning
approaches (i.e. the extremely quick succession of the SoTA), the main
contributions of the paper are its contextualization of modern deep learning
architectures with historical background and the elucidation of the current
trajectory of volumetric medical image segmentation research.
