  Recently, deep convolutional neural networks have achieved great success for
medical image segmentation. However, unlike segmentation of natural images,
most medical images such as MRI and CT are volumetric data. In order to make
full use of volumetric information, 3D CNNs are widely used. However, 3D CNNs
suffer from higher inference time and computation cost, which hinders their
further clinical applications. Additionally, with the increased number of
parameters, the risk of overfitting is higher, especially for medical images
where data and annotations are expensive to acquire. To issue this problem,
many 2.5D segmentation methods have been proposed to make use of volumetric
spatial information with less computation cost. Despite these works lead to
improvements on a variety of segmentation tasks, to the best of our knowledge,
there has not previously been a large-scale empirical comparison of these
methods. In this paper, we aim to present a review of the latest developments
of 2.5D methods for volumetric medical image segmentation. Additionally, to
compare the performance and effectiveness of these methods, we provide an
empirical study of these methods on three representative segmentation tasks
involving different modalities and targets. Our experimental results highlight
that 3D CNNs may not always be the best choice. Despite all these 2.5D methods
can bring performance gains to 2D baseline, not all the methods hold the
benefits on different datasets. We hope the results and conclusions of our
study will prove useful for the community on exploring and developing efficient
volumetric medical image segmentation methods.
