Share Email Print

Proceedings Paper

Deep-learning-based CT-CBCT image registration for adaptive radio therapy
Format Member Price Non-Member Price
PDF $17.00 $21.00

Paper Abstract

While deep learning based methods for medical deformable image registration have recently shown significant advances in both speed and accuracy, methods for use in radio therapy are still rarely proposed due to several challenges such as low contrast and artifacts in cone beam CT (CBCT) images or extreme deformations. The aim of image registration in radio therapy is to align a baseline CT and low-dose CBCT images, which allows contours to be propagated and applied doses to be tracked over time. To this end, we present a novel deep learning method for multi-modal deformable CT-CBCT registration. We train a CNN in weakly supervised manner, aiming to optimize an edge-based image similarity and a deformation regularizer including a penalty for local changes of topology and foldings. Additionally, we measure the alignment of given segmentations, facing the problem of extreme deformations. Our method receives only CT and a CBCT images as input and uses groundtruth segmentations exclusively during training. Furthermore, our method is not dependent on the availability of difficult to access ground-truth deformation vector fields. We train and evaluate our method on follow-up image pairs of the pelvis and compare our results to conventional iterative registration algorithms. Our experiments show that the registration accuracy of our deep learning based approach is superior to iterative registration without additional guidance by segmentations and nearly as good as iterative structure guided registration that requires ground-truth segmentations. Furthermore, our deep learning based method runs approximately 100 times faster than the iterative methods.

Paper Details

Date Published: 10 March 2020
PDF: 6 pages
Proc. SPIE 11313, Medical Imaging 2020: Image Processing, 113130Q (10 March 2020); doi: 10.1117/12.2549531
Show Author Affiliations
Sven Kuckertz, Fraunhofer Institute for Digital Medicine MEVIS (Germany)
Nils Papenberg, Fraunhofer Institute for Digital Medicine MEVIS (Germany)
Jonas Honegger, Varian Medical Systems (Switzerland)
Tomasz Morgas, Varian Medical Systems (Switzerland)
Benjamin Haas, Varian Medical Systems (Switzerland)
Stefan Heldmann, Fraunhofer Institute for Digital Medicine MEVIS (Germany)

Published in SPIE Proceedings Vol. 11313:
Medical Imaging 2020: Image Processing
Ivana Išgum; Bennett A. Landman, Editor(s)

© SPIE. Terms of Use
Back to Top
Sign in to read the full article
Create a free SPIE account to get access to
premium articles and original research
Forgot your username?