Optimized liver tumor margin assessment using contrastive learning and hyperspectral imaging

Liver cancer is a serious threat to people all over the world. Surgical resection is the preferred treatment for long-term survival. The residual malignant tissue will cause frequent recurrence of the disease and lead to high mortality. Therefore, the accurate determination of tumor resection margin is very important for surgical treatment. On the one hand, the ultimate goal of surgical resection is to remove all liver cancer tissue as much as possible to avoid residual; On the other hand, it is necessary to preserve as much healthy tissue as possible and maintain the basic functions of the organs. Hyperspectral imaging technology can quickly and accurately capture the spatial and spectral information of liver tumor tissue, and realize the accurate division of liver tumor incisal margin during surgery. The two goals mentioned above essentially correspond to the sensitivity and specificity in performance evaluation. In this work, we used the idea of contrastive learning to improve the classical U-net backbone network and optimize the overall performance of the liver tumor classification network. Specifically, a dataset containing 36 specimens was collected from 19 patients and pathological results were used as ground truth. The mean value of the improved network classification results increased and the standard deviation decreased in terms of accuracy, sensitivity and specificity , especially the sensitivity and specificity were significantly improved, with the overall sensitivity reaching 96.61% and the specificity reaching 95.00%, which met the original intention of surgical resection, and was of great significance for the practical application.