"Adversarial optimization for joint registration and segmentation in prostate CT radiotherapy"

Mohamed S. Elmahdy, Jelmer M. Wolterink, Hessam Sokooti, Ivana Išgum and Marius Staring


Joint image registration and segmentation has long been an active area of research in medical imaging. Here, we reformulate this problem in a deep learning setting using adversarial learning. We consider the case in which fixed and moving images as well as their segmentations are available for training, while segmentations are not available during testing; a common scenario in radiotherapy. The proposed framework consists of a 3D end-to-end generator network that estimates the deformation vector field (DVF) between fixed and moving images in an unsupervised fashion and applies this DVF to the moving image and its segmentation. A discriminator network is trained to evaluate how well the moving image and segmentation align with the fixed image and segmentation. The proposed network was trained and evaluated on follow-up prostate CT scans for image-guided radiotherapy, where the planning CT contours are propagated to the daily CT images using the estimated DVF. A quantitative comparison with conventional registration using elastix showed that the proposed method improved performance and substantially reduced computation time, thus enabling real-time contour propagation necessary for online-adaptive radiotherapy.



PDF (9 pages, 997 kB) click to start download
From publisher link

Copyright © 2019 by the authors. Published version © 2019 by Springer Lecture Notes in Computer Science. Personal use of this material is permitted. However, permission to reprint or republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works, must be obtained from the copyright holder.


BibTeX entry

author = "{Mohamed S. Elmahdy and Jelmer M. Wolterink and Hessam Sokooti and Ivana Išgum and Marius Staring}",
title = "{Adversarial optimization for joint registration and segmentation in prostate CT radiotherapy}",
booktitle = "{Medical Image Computing and Computer-Assisted Intervention}",
editor = "{Dinggang Shen and Tianming Liu and Terry M. Peters and Lawrence H. Staib and Caroline Essert and Sean Zhou and Pew-Thian Yap and Ali Khan}",
address = "{Shenzhen, China}",
series = "{Lecture Notes in Computer Science}",
volume = "{11769}",
pages = "{366 - 374}",
month = "{October}",
year = "{2019}",

last modified: 30-10-2019 |webmaster |Copyright 2004-2020 © by Marius Staring