Back to Search Start Over

2D/3D Deep Image Registration by Learning 3D Displacement Fields for Abdominal Organs

Authors :
Miura, Ryuto
Nakao, Megumi
Nakamura, Mitsuhiro
Matsuda, Tetsuya
Publication Year :
2022

Abstract

Deformable registration of two-dimensional/three-dimensional (2D/3D) images of abdominal organs is a complicated task because the abdominal organs deform significantly and their contours are not detected in two-dimensional X-ray images. We propose a supervised deep learning framework that achieves 2D/3D deformable image registration between 3D volumes and single-viewpoint 2D projected images. The proposed method learns the translation from the target 2D projection images and the initial 3D volume to 3D displacement fields. In experiments, we registered 3D-computed tomography (CT) volumes to digitally reconstructed radiographs generated from abdominal 4D-CT volumes. For validation, we used 4D-CT volumes of 35 cases and confirmed that the 3D-CT volumes reflecting the nonlinear and local respiratory organ displacement were reconstructed. The proposed method demonstrate the compatible performance to the conventional methods with a dice similarity coefficient of 91.6 \% for the liver region and 85.9 \% for the stomach region, while estimating a significantly more accurate CT values.

Details

Database :
arXiv
Publication Type :
Report
Accession number :
edsarx.2212.05445
Document Type :
Working Paper