Using Deep Learning to Evaluate the Segmentation of Liver Cell from Biopsy Image

Liver cancer is one of the most critical health problems in the world. The grading diagnosis for liver cancer in biopsy images is essential for the treatment of liver cancer and disease prognosis. A grading system that uses artificial intelligence to provide quantitative and objective results for physicians and pathologists; it will not only save time but also improve the accuracy of diagnosis. In the grading system, the main work is grading with the nucleus segmented from cancer biopsy images. However, improper focus and complex stroma background will affect the performance of segmentation. If we can evaluate segmentation of the nucleus and exclude the failed segmentation from the grading system, it will significantly improve the accuracy of the grading. In this paper, we propose a method with deep learning for evaluating the segmentation of liver nucleus, and the experimental results demonstrate that the performance of our method is 90.5%.