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Image segmentation plays a pivotal role in several medical-imagingapplications by assisting the segmentation of the regions of interest. Deeplearning-based approaches have been widely adopted for semantic segmentation ofmedical data. In recent years, in addition to 2D deep learning architectures,3D architectures have been employed as the predictive algorithms for 3D medicalimage data. In this paper, we propose a 3D stack-based deep learning techniquefor segmenting manifestations of consolidation and ground-glass opacities in 3DComputed Tomography (CT) scans. We also present a comparison based on thesegmentation results, the contextual information retained, and the inferencetime between this 3D technique and a traditional 2D deep learning technique. Wealso define the area-plot, which represents the peculiar pattern observed inthe slice-wise areas of the pathology regions predicted by these deep learningmodels. In our exhaustive evaluation, 3D technique performs better than the 2Dtechnique 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 5Xreduction in the inference time compared to the 2D technique. Results also showthat the area-plots predicted by the 3D model are more similar to the groundtruth than those predicted by the 2D model. We also show how increasing theamount of contextual information retained during the training can improve the3D model's performance.

Comparative Evaluation of 3D and 2D Deep Learning Techniques for Semantic Segmentation in CT Scans