Construction Of A Generative Model Of H&E Stained Pathology Images Of Pancreas Tumors Conditioned By A Voxel Value Of Mri Image

In this paper, we propose a method for constructing a multiscale model of pancreas tumor of a KrasLSL. G12D/+; p53R172H/+; PdxCretg/+ (KPC) mouse that is a genetically engineered mouse model of pancreas tumor. The model represents the correlation between the value at each voxel in the MRI image of the tumor and the pathology image patches that are observed at each portion corresponds to the location of the voxel in the MRI image. The model is represented by a cascade of image generators trained by a Laplacian Pyramid of Generative Adversarial Network (LAPGAN). When some voxel in a pancreas tumor region in an MRI image is selected, the cascade of generators outputs patches of the pathology images that can be observed at the location corresponds to the selected voxel. We trained the generators by using an MRI image and a 3D pathology image, the latter was first reconstructed from a spatial series of the 2D pathology images and was then registered to the MRI image.