<?xml version="1.0" encoding="UTF-8"?><xml><records><record><source-app name="Biblio" version="6.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Andresen, Julia</style></author><author><style face="normal" font="default" size="100%">Timo Kepp</style></author><author><style face="normal" font="default" size="100%">Jan Ehrhardt</style></author><author><style face="normal" font="default" size="100%">von der Burchard, Claus</style></author><author><style face="normal" font="default" size="100%">Roider, Johann</style></author><author><style face="normal" font="default" size="100%">Heinz Handels</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Deep learning-based simultaneous registration and unsupervised non-correspondence segmentation of medical images with pathologies</style></title><secondary-title><style face="normal" font="default" size="100%">International Journal of Computer Assisted Radiology and Surgery</style></secondary-title><short-title><style face="normal" font="default" size="100%">Int J CARS</style></short-title></titles><dates><year><style  face="normal" font="default" size="100%">2022</style></year></dates><abstract><style face="normal" font="default" size="100%">The registration of medical images often suffers from missing correspondences due to inter-patient variations, pathologies and their progression leading to implausible deformations that cause misregistrations and might eliminate valuable information. Detecting non-corresponding regions simultaneously with the registration process helps generating better deformations and has been investigated thoroughly with classical iterative frameworks but rarely with deep learning-based methods.</style></abstract><custom1><style face="normal" font="default" size="100%">https://pubmed.ncbi.nlm.nih.gov/35239133</style></custom1><custom3><style face="normal" font="default" size="100%">http://www.ncbi.nlm.nih.gov/pmc/articles/pmc8948150/</style></custom3></record></records></xml>