Details

Autor(en) / Beteiligte

• Li, Peixin
• Pei, Yuru
• Guo, Yuke
• Ma, Gengyu
• Xu, Tianmin
• Zha, Hongbin

Titel

Non-Rigid 2D-3D Registration Using Convolutional Autoencoders

Ist Teil von

• 2020 IEEE 17th International Symposium on Biomedical Imaging (ISBI), 2020, p.700-704

Ort / Verlag

IEEE

Erscheinungsjahr

2020

Link zum Volltext

• IEEE_Xplore

Quelle

IEEE Xplore

Beschreibungen/Notizen

• In this paper, we propose a novel neural network-based framework for the non-rigid 2D-3D registration of the lateral cephalogram and the volumetric cone-beam CT (CBCT) images. The task is formulated as an embedding problem, where we utilize the statistical volumetric representation and embed the X-ray image to a code vector regarding the non-rigid volumetric deformations. In particular, we build a deep ResNet-based encoder to infer the code vector from the input X-ray image. We design a decoder to generate digitally reconstructed radiographs (DRRs) from the non-rigidly deformed volumetric image determined by the code vector. The parameters of the encoder are optimized by minimizing the difference between synthetic DRRs and input X-ray images in an unsupervised way. Without geometric constraints from multi-view X-ray images, we exploit structural constraints of the multi-scale feature pyramid in similarity analysis. The training process is unsupervised and does not require paired 2D X-ray images and 3D CBCT images. The system allows constructing a volumetric image from a single X-ray image and realizes the 2D-3D registration between the lateral cephalograms and CBCT images.