Deep learning-based semantic segmentation of non-melanocytic skin tumors in whole-slide histopathological images.

Basal cell carcinoma (BCC) and squamous cell carcinoma (SCC) are the two most common skin cancer and impose a huge medical burden on society. Histopathological examination based on whole-slide images (WSIs) remains to be the confirmatory diagnostic method for skin tumors. Accurate segmentation of tumor tissue in WSIs by deep-learning (DL) models can reduce the workload of pathologists and help surgeons ensure the complete removal of tumors. To accurately segment the tumor areas in WSIs of BCC, SCC and squamous cell papilloma (SCP, homologous to SCC) with robust models. We established a data set (ZJU-NMSC) containing 151 WSIs of BCC, SCC and SCP in total. Seven models were utilized to segment WSIs, including the state-of-the-art model, models proposed by us and other models. Dice score, intersection over union, accuracy, sensitivity and specificity were used to evaluate and compare the performance of different models. Heatmaps and tumor tissue masks were generated to reflect the results of the segmentation. The processing times of models are also recorded and compared. While the dice score of most models is higher than 0.85, deeplab v3+ has the best performance and the corresponding tumor tissue mask is more consistent with the ground truth tumor areas even with complex and small lobular lesions. This study broadens the use of DL-based segmentation models in WSIs of skin tumors in terms of tumor types and computational approaches. Segmenting tumor areas can simplify the process of histopathological inspection and benefit the diagnosis and following management of the diseases in practice.