Abstract: A machine-agnostic segmentation and quantification method for coronavirus diagnostic includes receiving computer tomograph, CT, raw scans; normalizing the CT raw scans to obtain normalized data, wherein the normalized data is normalized in terms of dimension, resolution, and signal intensity; generating augmented data based on (1) the CT raw scans and (2) a simulation model; segmenting three different 2-dimensional, 2D, images from the normalized data, which correspond to a same voxel, , using three functions , and , respectively; and quantizing (508) each voxel to have a value of 0 or 1, based on the three functions , and and an aggregation function g. The value 0 indicates that the voxel is not infected with the coronavirus, and the value 1 indicates that the voxel is infected with the coronavirus, and the three functions , and are trained based on the augmented data.