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During the last decade, the digitization of pathology has gained considerablemomentum. Digital pathology offers many advantages including more efficientworkflows, easier collaboration as well as a powerful venue for telepathology.At the same time, applying Computer-Aided Diagnosis (CAD) on Whole Slide Images(WSIs) has received substantial attention as a direct result of thedigitization. The first step in any image analysis is to extract the tissue.Hence, background removal is an essential prerequisite for efficient andaccurate results for many algorithms. In spite of the obvious discriminationfor human operators, the identification of tissue regions in WSIs could bechallenging for computers, mainly due to the existence of color variations andartifacts. Moreover, some cases such as alveolar tissue types, fatty tissues,and tissues with poor staining are difficult to detect. In this paper, weperform experiments on U-Net architecture with different network backbones(different topologies) to remove the background as well as artifacts from WSIsin order to extract the tissue regions. We compare a wide range of backbonenetworks including MobileNet, VGG16, EfficientNet-B3, ResNet50, ResNext101 andDenseNet121. We trained and evaluated the network on a manually labeled subsetof The Cancer Genome Atlas (TCGA) Dataset. EfficientNet-B3 and MobileNet byalmost 99% sensitivity and specificity reached the best results.

A Comparative Study of U-Net Topologies for Background Removal in Histopathology Images