Segmentation of nuclei in hepatic histological images using multimodal method

Nuclear density and nuclear morphometric features such as shape of nuclei are useful in diagnosing hepatocellular carcinoma, especially well-differentiated hepatocellular carcinoma (ewHCC). We previously developed a support system for diagnosing ewHCC that enables the user to estimate the nuclear density and the roundness factor of nuclei. The system automatically extracts the positions and contours of nuclei from a microscopic image. However, it takes a few minutes for the user to correct wrong positions and contours using a graphical user interface (GUI). Our target is to improve the accuracy for nuclear position extraction and contours extraction and to make the system more convenient. As a method to improve the accuracy, a multimodal method was employed. A multimodal method is a method to use images captured by more than one imaging methods. We found a composite image of bright-field and dark-field images improves the accuracy. Experimental results showed that the accuracy was improved by the multimodal method as well as by some additional improvements in the method.