Self-Supervised 2D/3D Registration for X-Ray to CT Image Fusion

TL;DR

This paper introduces a self-supervised framework for 2D/3D registration that effectively bridges the domain gap between simulated and real X-ray images, enabling accurate X-ray to CT image fusion without paired datasets.

Contribution

It presents a novel self-supervised approach combining simulated training with unsupervised domain adaptation for improved registration accuracy.

Findings

Achieves 1.83mm registration accuracy on real X-ray images.

Attains a 90.1% success ratio, 23.9% higher than previous annotation-free methods.

Effectively reduces the domain gap between simulated and real X-ray images.

Abstract

Deep Learning-based 2D/3D registration enables fast, robust, and accurate X-ray to CT image fusion when large annotated paired datasets are available for training. However, the need for paired CT volume and X-ray images with ground truth registration limits the applicability in interventional scenarios. An alternative is to use simulated X-ray projections from CT volumes, thus removing the need for paired annotated datasets. Deep Neural Networks trained exclusively on simulated X-ray projections can perform significantly worse on real X-ray images due to the domain gap. We propose a self-supervised 2D/3D registration framework combining simulated training with unsupervised feature and pixel space domain adaptation to overcome the domain gap and eliminate the need for paired annotated datasets. Our framework achieves a registration accuracy of 1.83$\pm$1.16 mm with a high success ratio of 90.1% on real X-ray images showing a 23.9% increase in success ratio compared to reference annotation-free algorithms.