# A Rigid Registration Method in TEVAR

arXiv: https://arxiv.org/abs/2104.14273 · HTML: https://mlsanity.com/paper/2104.14273/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 7 of its 21 references that are in the map.
- First posted on arXiv 29 Apr 2021.

## Builds on (most cited first, 7 of 7)

- [U-Net: Convolutional Networks for Biomedical Image Segmentation](https://mlsanity.com/paper/1505.04597/) (2015)
- [3D U-Net: Learning Dense Volumetric Segmentation from Sparse Annotation](https://mlsanity.com/paper/1606.06650/) (2016)
- [Generative Adversarial Network in Medical Imaging: A Review](https://mlsanity.com/paper/1809.07294/) (2018)
- [Dilated FCN for Multi-Agent 2D/3D Medical Image Registration](https://mlsanity.com/paper/1712.01651/) (2017)
- [Multiview 2D/3D Rigid Registration via a Point-Of-Interest Network for Tracking and Triangulation (POINT2)](https://mlsanity.com/paper/1903.03896/) (2019)
- [FusionNet: A deep fully residual convolutional neural network for image segmentation in connectomics](https://mlsanity.com/paper/1612.05360/) (2016)
- [Deep Deformable Registration: Enhancing Accuracy by Fully Convolutional Neural Net](https://mlsanity.com/paper/1611.08796/) (2016)

## Abstract (arXiv)

Since the mapping relationship between definitized intra-interventional X-ray and undefined pre-interventional Computed Tomography(CT) is uncertain, auxiliary positioning devices or body markers, such as medical implants, are commonly used to determine this relationship. However, such approaches can not be widely used in clinical due to the complex realities. To determine the mapping relationship, and achieve a initializtion post estimation of human body without auxiliary equipment or markers, proposed method applies image segmentation and deep feature matching to directly match the X-ray and CT images. As a result, the well-trained network can directly predict the spatial correspondence between arbitrary X-ray and CT. The experimental results show that when combining our approach with the conventional approach, the achieved accuracy and speed can meet the basic clinical intervention needs, and it provides a new direction for intra-interventional registration.