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

arXiv: https://arxiv.org/abs/2212.05445 · HTML: https://mlsanity.com/paper/2212.05445/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 5 of its 13 references that are in the map.
- First posted on arXiv 11 Dec 2022.

## Builds on (most cited first, 5 of 5)

- [U-Net: Convolutional Networks for Biomedical Image Segmentation](https://mlsanity.com/paper/1505.04597/) (2015)
- [VoxelMorph: A Learning Framework for Deformable Medical Image Registration](https://mlsanity.com/paper/1809.05231/) (2018)
- [Deep Learning in Medical Image Registration: A Survey](https://mlsanity.com/paper/1903.02026/) (2019)
- [Image-to-Graph Convolutional Network for Deformable Shape Reconstruction from a Single Projection Image](https://mlsanity.com/paper/2108.12533/) (2021)
- [Statistical Deformation Reconstruction Using Multi-organ Shape Features for Pancreatic Cancer Localization](https://mlsanity.com/paper/1911.05439/) (2019)

## Abstract (arXiv)

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.