Structure-aware registration network for liver DCE-CT images
This work addresses image registration for liver cancer diagnosis and surgical planning, offering an incremental improvement by incorporating structural information to handle contrast agent effects and unpaired segmentations.
The authors tackled the problem of registering liver DCE-CT images, which is challenging due to intensity variations and respiratory motion, by proposing a structure-aware method that uses hierarchical geometric information to improve accuracy and preserve organ topology. Their method achieved higher registration accuracy and better anatomical structure preservation compared to state-of-the-art methods in experiments on in-house and public datasets.
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.