A Hybrid Method of Superpixel Segmentation Algorithm and Deep Learning Method in Histopathological Image Segmentation

Abstract

Manual analysis of cell morphology in high resolutional histopathological images is a tedious and time consuming task for pathologists. In recent years, computer assisted diagnostic systems have gained considerable importance in order to assist the pathologists for analyzing cellular structures. In this study, the simple linear iterative clustering (SLIC) superpixel segmentation method and convolutional neural network are combined to segment the cellular structures in histopathological images. The proposed study is mainly composed of two stages. First, SLIC superpixel method was used as a pre-segmentation algorithm to perform segmentation of cellular superpixels and non-cellular superpixels. Then convolutional neural networks (CNN) based deep learning algorithm is used to classify those superpixels in order to obtain the final segmentation of the whole image. The overall accuracy of the system at classifying the superpixels was observed to be 0.9876. The analysis and confusion matrix of the study was also presented in experimental studies section.