Efficient semantic segmentation for computational pathology

In digital and computational pathology, semantic segmentation can be considered as the first step toward assessing tissue specimens, providing the essential information for various downstream tasks. There exist numerous semantic segmentation methods and these often face challenges as they are applied to whole slide images, which are high-resolution and gigapixel-sized, and thus require a large amount of computation. In this study, we investigate the feasibility of an efficient semantic segmentation approach for whole slide images, which only processes the low-resolution pathology images to obtain the semantic segmentation results as equivalent as the results that can be attained by using high-resolution images. We employ five advanced semantic segmentation models and conduct three types of experiments to quantitatively and qualitatively test the feasibility of the efficient semantic segmentation approach. The quantitative experimental results demonstrate that, provided with low-resolution images, the semantic segmentation methods are inferior to those with high-resolution images. However, using low-resolution images, there is a substantial reduction in the computational cost. Furthermore, the qualitative analysis shows that the results obtained from low-resolution images are comparable to those from high-resolution images, suggesting the feasibility of the low-to-high semantic segmentation in computational pathology.