Tumor segmentation in whole slide images using persistent homology and deep convolutional features

Abstract

This paper presents a novel automated tumor segmentation approach for Hematoxylin & Eosin stained histology images. The proposed method enhances the segmentation performance by combining the topological and convolution neural network (CNN) features. Our approach is based on 3 steps: (1) construct enhanced persistent homology profiles by using topological features; (2) train a CNN to extract convolutional features; (3) employ a multi-stage ensemble strategy to combine Random Forest regression models. The experimental results demonstrate that proposed method outperforms the conventional CNN.