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

Research Articie June 10,2021

Xiao Liang ¹ , Howard Morgan ² , Dan Nguyen ³ , Steve Jiang ⁴ hide author's information

1 : Medical Artificial Intelligence and Automation Laboratory and Department of Radiation Oncology, University of Texas Southwestern Medical Center, Dallas, TX, USA
2 : Medical Artificial Intelligence and Automation Laboratory and Department of Radiation Oncology, University of Texas Southwestern Medical Center, Dallas, TX, USA
3 : Medical Artificial Intelligence and Automation Laboratory and Department of Radiation Oncology, University of Texas Southwestern Medical Center, Dallas, TX, USA
4 : Medical Artificial Intelligence and Automation Laboratory and Department of Radiation Oncology, University of Texas Southwestern Medical Center, Dallas, TX, USA

DOI: https://doi.org/10.2991/jaims.d.210527.001

Keywords: Deep learning; Deformable image registration; Segmentation; CBCT

Cite this article: Liang X, Morgan H, Nguyen D, Jiang S. Deep Learning–Based CT-to-CBCT Deformable Image Registration for Autosegmentation in Head and Neck Adaptive Radiation Therapy. JAIMS [Internet]. 2021;2(1-2):62-75.

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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. ...