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

• URL: http://arxiv.org/abs/2212.05445v1
• Date: Sun, 11 Dec 2022 08:36:23 GMT
• Title: 2D/3D Deep Image Registration by Learning 3D Displacement Fields for Abdominal Organs
• Authors: Ryuto Miura, Megumi Nakao, Mitsuhiro Nakamura, and Tetsuya Matsuda
• Abstract summary: 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.
• Score: 1.9949261242626626
• License: http://creativecommons.org/licenses/by/4.0/
• 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.

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