  In medical imaging analysis, deep learning has shown promising results. We
frequently rely on volumetric data to segment medical images, necessitating the
use of 3D architectures, which are commended for their capacity to capture
interslice context. However, because of the 3D convolutions, max pooling,
up-convolutions, and other operations utilized in these networks, these
architectures are often more inefficient in terms of time and computation than
their 2D equivalents. Furthermore, there are few 3D pretrained model weights,
and pretraining is often difficult. We present a simple yet effective 2D method
to handle 3D data while efficiently embedding the 3D knowledge during training.
We propose transforming volumetric data into 2D super images and segmenting
with 2D networks to solve these challenges. Our method generates a
super-resolution image by stitching slices side by side in the 3D image. We
expect deep neural networks to capture and learn these properties spatially
despite losing depth information. This work aims to present a novel perspective
when dealing with volumetric data, and we test the hypothesis using CNN and ViT
networks as well as self-supervised pretraining. While attaining equal, if not
superior, results to 3D networks utilizing only 2D counterparts, the model
complexity is reduced by around threefold. Because volumetric data is
relatively scarce, we anticipate that our approach will entice more studies,
particularly in medical imaging analysis.
