Graph Convolution Networks for Cell Segmentation

Abstract

Graph signal processing is an emerging field in deep learning, aiming to solve various non-Euclidean domain problems. Pathologist have difficulty detecting diseases at an early stage due to the limitations of clinical methods and image analysis. For more accurate diagnosis of disease and early detection, automated segmentation can play a vital role. However, efficiency and accuracy of the system depends on how the model learned. We have found that traditional machine-learning methods, such as clustering and thresholding, are unsuited for precise cell segmentation. Furthermore, the recent development of deep-learning techniques has demonstrated promising results, especially for medical images. In this paper, we proposed two graph-based convolution methods for cell segmentation to improve analysis of immunostained slides. Our proposed methods use advanced deep-learning, spectral-, and spatial-based graph signal processing approaches to learn features. We have compared our results with state-of-the-art fully convolutional networks(FCN) method and found a significant of improvement of 2.2% in the spectral-based approach and 3.94% in the spatial-based approach in pixel based accuracy.