  When using Convolutional Neural Networks (CNNs) for segmentation of organs
and lesions in medical images, the conventional approach is to work with inputs
and outputs either as single slice (2D) or whole volumes (3D). One common
alternative, in this study denoted as pseudo-3D, is to use a stack of adjacent
slices as input and produce a prediction for at least the central slice. This
approach gives the network the possibility to capture 3D spatial information,
with only a minor additional computational cost. In this study, we
systematically evaluate the segmentation performance and computational costs of
this pseudo-3D approach as a function of the number of input slices, and
compare the results to conventional end-to-end 2D and 3D CNNs. The standard
pseudo-3D method regards the neighboring slices as multiple input image
channels. We additionally evaluate a simple approach where the input stack is a
volumetric input that is repeatably convolved in 3D to obtain a 2D feature map.
This 2D map is in turn fed into a standard 2D network. We conducted experiments
using two different CNN backbone architectures and on five diverse data sets
covering different anatomical regions, imaging modalities, and segmentation
tasks. We found that while both pseudo-3D methods can process a large number of
slices at once and still be computationally much more efficient than fully 3D
CNNs, a significant improvement over a regular 2D CNN was only observed for one
of the five data sets. An analysis of the structural properties of the
segmentation masks revealed no relations to the segmentation performance with
respect to the number of input slices. The conclusion is therefore that in the
general case, multi-slice inputs appear to not significantly improve
segmentation results over using 2D or 3D CNNs.
