Journal of Artificial Intelligence for Medical Sciences

Volume 2, Issue 1-2, June 2021, Pages 62 - 75

Deep Learning–Based CT-to-CBCT Deformable Image Registration for Autosegmentation in Head and Neck Adaptive Radiation Therapy

Authors
Xiao LiangORCID, Howard Morgan*, ORCID, Dan Nguyen, Steve Jiang
Medical Artificial Intelligence and Automation Laboratory and Department of Radiation Oncology, University of Texas Southwestern Medical Center, Dallas, TX, USA
*Corresponding author. Email: Steve.Jiang@UTSouthwestern.edu
Corresponding Author
Howard Morgan
Received 31 January 2021, Accepted 25 May 2021, Available Online 10 June 2021.
DOI
https://doi.org/10.2991/jaims.d.210527.001How to use a DOI?
Keywords
Deep learning, Deformable image registration, Segmentation, CBCT
Abstract

The purpose of this study is to develop a deep learning–based method that can automatically generate segmentations on cone-beam computed tomography (CBCT) for head and neck online adaptive radiation therapy (ART), where expert-drawn contours in planning CT (pCT) images serve as prior knowledge. Because of the many artifacts and truncations that characterize CBCT, we propose to utilize a learning-based deformable image registration method and contour propagation to get updated contours on CBCT. Our method takes CBCT and pCT as inputs, and it outputs a deformation vector field and synthetic CT (sCT) simultaneously by jointly training a CycleGAN model and 5-cascaded Voxelmorph model. The CycleGAN generates the sCT from CBCT, while the 5-cascaded Voxelmorph warps the pCT to the sCT's anatomy. We compared the segmentation results to Elastix, Voxelmorph and 5-cascaded Voxelmorph models on 18 structures including target and organs-at-risk. Our proposed method achieved an average Dice similarity coefficient of 0.83 ± 0.09 and an average 95% Hausdorff distance of 2.01 ± 1.81 mm. Our method showed better accuracy than Voxelmorph and 5-cascaded Voxelmorph and comparable accuracy to Elastix, but with much higher efficiency. The proposed method can rapidly and simultaneously generate sCT with correct CT numbers and propagate contours from pCT to CBCT for online ART replanning.

Copyright
© 2021 The Authors. Published by Atlantis Press B.V.
Open Access
This is an open access article distributed under the CC BY-NC 4.0 license (http://creativecommons.org/licenses/by-nc/4.0/).

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Journal
Journal of Artificial Intelligence for Medical Sciences
Volume-Issue
2 - 1-2
Pages
62 - 75
Publication Date
2021/06/10
ISSN (Online)
2666-1470
DOI
https://doi.org/10.2991/jaims.d.210527.001How to use a DOI?
Copyright
© 2021 The Authors. Published by Atlantis Press B.V.
Open Access
This is an open access article distributed under the CC BY-NC 4.0 license (http://creativecommons.org/licenses/by-nc/4.0/).

Cite this article

TY  - JOUR
AU  - Xiao Liang
AU  - Howard Morgan
AU  - Dan Nguyen
AU  - Steve Jiang
PY  - 2021
DA  - 2021/06/10
TI  - Deep Learning–Based CT-to-CBCT Deformable Image Registration for Autosegmentation in Head and Neck Adaptive Radiation Therapy
JO  - Journal of Artificial Intelligence for Medical Sciences
SP  - 62
EP  - 75
VL  - 2
IS  - 1-2
SN  - 2666-1470
UR  - https://doi.org/10.2991/jaims.d.210527.001
DO  - https://doi.org/10.2991/jaims.d.210527.001
ID  - Liang2021
ER  -