Visualization of Features in Whole Slide Images of Early Well-Differentiated Hepatocellular Carcinoma

Abstract

Differential diagnosis of early well-differentiated hepatocellular carcinoma （ewHCC） from non-cancer is very difficult because cellular and structural atypism in most ewHCCʼs are very slight. We have realized a method to visualize the distribution of nuclear density in whole slide images of ewHCC sections. While nuclear density can help diagnose ewHCC, it is desirable to visualize the distribution of more features to enhance the usefulness of the function. We have thus realized an automatic method of re-extracting the contours of the cell nuclei and visualizing the distribution of shape features including circularity, which is useful for diagnosis. The extracted shape features are circularity, a ratio of major axis to minor axis, a standard deviation of distance between the center of gravity and contours, and a nuclear area. The mean absolute percentage errors for the extracted features were 0.26％, 2.02％, 9.75％ and 6.94％, respectively. All the processing is automated, and the computation time on a PC is less than an hour even for large surgical sections.