Artificial Intelligence for Infectious Disease Imaging

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Abstract

A CNN architecture was used to create a deep learning-based segmentation model of the liver seen on CT scans of a nonhuman primate (NHP). The CNN architecture was optimized by implementing a feature pyramid network (FPN). This FPN model was then utilized to generalize liver segmentation to other organs such as the spleen and then the whole lung and lung lesions in NHP models of SARS-CoV-2 infection, as conducted at the IRF. Lung lobe segmentation was developed in order to better correlate with histopathologic findings of COVID-19. This is a work-in-progress as the number of lung lobe annotations on CT scans is at a minimum due to the difficulty in creating this ground truth data. Currently, methods that function with low numbers of training data are being explored. To make these deep learning-based image segmentation methods available for use by the imaging community at the IRF, an automated pipeline process was developed. This pipeline utilizes the NIAID high performance computing environment (Locus) to allow for enhanced parallel processing with graphical processing units (GPUs). These methods are being used by other imaging researchers at IRF to post-process images acquired during infectious disease imaging studies of SARS-CoV-2, Marburg, Ebola, Nipah and Lassa viruses. In addition, these segmentations provide regions of interest for radiomic feature extraction. These hundreds of radiomic features have been input into feature selections methods, such as maximum relevance minimum redundancy (mRMR), to reduce and optimize features for predictive analyses. In addition, a novel feature selection method was developed (mRMR-permute), which uses permutation testing to automatically limit the number of features chosen. The predictive analyses are implemented with conventional machine learning methods that were explored to determine optimal use for SARS-CoV-2 imaging experiments. To further the specificity of image segmentations, a lesion phenotype classification algorithm is being developed. As an initial implementation, lung lesions seen in CT scans are to be classified as ground glass opacities or consolidations. This classification will help with severity assessment during longitudinal quantification.