  Image segmentation plays a pivotal role in several medical-imaging
applications by assisting the segmentation of the regions of interest. Deep
learning-based approaches have been widely adopted for semantic segmentation of
medical data. In recent years, in addition to 2D deep learning architectures,
3D architectures have been employed as the predictive algorithms for 3D medical
image data. In this paper, we propose a 3D stack-based deep learning technique
for segmenting manifestations of consolidation and ground-glass opacities in 3D
Computed Tomography (CT) scans. We also present a comparison based on the
segmentation results, the contextual information retained, and the inference
time between this 3D technique and a traditional 2D deep learning technique. We
also define the area-plot, which represents the peculiar pattern observed in
the slice-wise areas of the pathology regions predicted by these deep learning
models. In our exhaustive evaluation, 3D technique performs better than the 2D
technique for the segmentation of CT scans. We get dice scores of 79% and 73%
for the 3D and the 2D techniques respectively. The 3D technique results in a 5X
reduction in the inference time compared to the 2D technique. Results also show
that the area-plots predicted by the 3D model are more similar to the ground
truth than those predicted by the 2D model. We also show how increasing the
amount of contextual information retained during the training can improve the
3D model's performance.
