Structure-aware registration network for liver DCE-CT images

by   Peng Xue, et al.

Image registration of liver dynamic contrast-enhanced computed tomography (DCE-CT) is crucial for diagnosis and image-guided surgical planning of liver cancer. However, intensity variations due to the flow of contrast agents combined with complex spatial motion induced by respiration brings great challenge to existing intensity-based registration methods. To address these problems, we propose a novel structure-aware registration method by incorporating structural information of related organs with segmentation-guided deep registration network. Existing segmentation-guided registration methods only focus on volumetric registration inside the paired organ segmentations, ignoring the inherent attributes of their anatomical structures. In addition, such paired organ segmentations are not always available in DCE-CT images due to the flow of contrast agents. Different from existing segmentation-guided registration methods, our proposed method extracts structural information in hierarchical geometric perspectives of line and surface. Then, according to the extracted structural information, structure-aware constraints are constructed and imposed on the forward and backward deformation field simultaneously. In this way, all available organ segmentations, including unpaired ones, can be fully utilized to avoid the side effect of contrast agent and preserve the topology of organs during registration. Extensive experiments on an in-house liver DCE-CT dataset and a public LiTS dataset show that our proposed method can achieve higher registration accuracy and preserve anatomical structure more effectively than state-of-the-art methods.


page 1

page 4

page 5

page 6

page 7

page 8


A multi-organ point cloud registration algorithm for abdominal CT registration

Registering CT images of the chest is a crucial step for several tasks s...

LiftReg: Limited Angle 2D/3D Deformable Registration

We propose LiftReg, a 2D/3D deformable registration approach. LiftReg is...

Automatic Registration between Cone-Beam CT and Scanned Surface via Deep-Pose Regression Neural Networks and Clustered Similarities

Computerized registration between maxillofacial cone-beam computed tomog...

Anatomically constrained CT image translation for heterogeneous blood vessel segmentation

Anatomical structures such as blood vessels in contrast-enhanced CT (ceC...

SAMConvex: Fast Discrete Optimization for CT Registration using Self-supervised Anatomical Embedding and Correlation Pyramid

Estimating displacement vector field via a cost volume computed in the f...

Pseudo-Label Guided Multi-Contrast Generalization for Non-Contrast Organ-Aware Segmentation

Non-contrast computed tomography (NCCT) is commonly acquired for lung ca...

𝒳-Metric: An N-Dimensional Information-Theoretic Framework for Groupwise Registration and Deep Combined Computing

This paper presents a generic probabilistic framework for estimating the...

Please sign up or login with your details

Forgot password? Click here to reset