However, in contrast to ICON, we prove and empirically verify that using a gradient inverse-consistency loss not only significantly improves convergence, but also results in a similar implicit regularization of the resulting transformation map.
no code implementations • 30 Mar 2020 • Germán González, Daniel Jimenez-Carretero, Sara Rodríguez-López, Carlos Cano-Espinosa, Miguel Cazorla, Tanya Agarwal, Vinit Agarwal, Nima Tajbakhsh, Michael B. Gotway, Jianming Liang, Mojtaba Masoudi, Noushin Eftekhari, Mahdi Saadatmand, Hamid-Reza Pourreza, Patricia Fraga-Rivas, Eduardo Fraile, Frank J. Rybicki, Ara Kassarjian, Raúl San José Estépar, Maria J. Ledesma-Carbayo
Objective: To generate a database of annotated computed tomography pulmonary angiographies, use it to compare the sensitivity and false positive rate of current algorithms and to develop new methods that improve such metrics.
For validation, we first use synthetically generated airways and vessels produced by the proposed generative model to compute the relative error and directly evaluate the accuracy of CNR in comparison with traditional methods.
In this paper, we present a novel, fully automatic approach to classify vessels from chest CT images into arteries and veins.
Ranked #3 on Pulmonary Artery–Vein Classification on SunYs