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

TL;DR

This paper introduces a supervised deep learning approach for deformable 2D/3D image registration of abdominal organs, effectively capturing nonlinear organ displacements from single-viewpoint 2D images to 3D volumes.

Contribution

It presents a novel framework that learns 3D displacement fields directly from 2D projections and initial 3D volumes, improving registration accuracy for abdominal organs.

Findings

Achieved a dice similarity coefficient of 91.6% for liver registration.

Demonstrated accurate reconstruction of nonlinear organ displacements.

Performed comparably to conventional methods with improved CT value estimation.

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.