  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.
