Fully automated segmentation of pneumonia infection based on Probabilistic Graphical Model and U-Net blend network

Abstract

ABSTRACT COVID-19 spread rapidly in the global world, causing a serious medical treatment crisis. Automated segmentation of pulmonary infection from Computed Tomography (CT) images strengthened the traditional treatment strategy against COVID-19. We proposed an automated fully lung CT image infection segmentation framework named Probabilistic Graphical Model U-Net (PGM-U-Net). The whole framework iterative training end-to-end with general back-propagation mechanism with minor computational overhead from PGM component. The U-Net feature extractor benefits from modelling spatial correlations through the PGM component. Compared to the baseline network without modelling spatial correlations, experimental results illustrate that the proposed PGM-U-Net framework achieves higher accuracy probability maps of region predictions in the isolated infection regions. For further quantitative comparison experiment, we demonstrate that our framework outperforms the existing methods in pulmonary infection segmentation and achieves the Free-response Receiver Operating Characteristic Curve (FROC) score of 0.912 on the test data set.