Adversarial Optimization for Joint Registration and Segmentation in Prostate CT Radiotherapy

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

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contributionAcademicpeer-review

2 Citations (Scopus)
3 Downloads (Pure)

Abstract

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.

Original languageEnglish
Title of host publicationMedical Image Computing and Computer Assisted Intervention – MICCAI 2019
Subtitle of host publication22nd International Conference, Shenzhen, China, October 13–17, 2019, Proceedings
EditorsDinggang Shen, Pew-Thian Yap, Tianming Liu, Terry M. Peters, Ali Khan, Lawrence H. Staib, Caroline Essert, Sean Zhou
Place of PublicationCham
PublisherSpringer Singapore
Pages366-374
Number of pages9
VolumeVI
ISBN (Electronic)978-3-030-32226-7
ISBN (Print)978-3-030-32225-0
DOIs
Publication statusPublished - 1 Jan 2019
Externally publishedYes
Event22nd International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2019 - InterContinental Shenzhen, Shenzhen, China
Duration: 13 Oct 201917 Oct 2019
Conference number: 22
https://www.miccai2019.org/

Publication series

NameLecture Notes in Computer Science
PublisherSpringer
Volume11769
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference22nd International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2019
Abbreviated titleMICCAI 2019
CountryChina
CityShenzhen
Period13/10/1917/10/19
Internet address

Keywords

  • Adversarial training
  • Contour propagation
  • Deformable image registration
  • Image segmentation
  • Radiotherapy

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  • Cite this

    Elmahdy, M. S., Wolterink, J. M., Sokooti, H., Išgum, I., & Staring, M. (2019). Adversarial Optimization for Joint Registration and Segmentation in Prostate CT Radiotherapy. In D. Shen, P-T. Yap, T. Liu, T. M. Peters, A. Khan, L. H. Staib, C. Essert, ... S. Zhou (Eds.), Medical Image Computing and Computer Assisted Intervention – MICCAI 2019: 22nd International Conference, Shenzhen, China, October 13–17, 2019, Proceedings (Vol. VI, pp. 366-374). (Lecture Notes in Computer Science; Vol. 11769). Cham: Springer Singapore. https://doi.org/10.1007/978-3-030-32226-7_41