  Multimodal image registration has many applications in diagnostic medical
imaging and image-guided interventions, such as Transcatheter Arterial
Chemoembolization (TACE) of liver cancer guided by intraprocedural CBCT and
pre-operative MR. The ability to register peri-procedurally acquired diagnostic
images into the intraprocedural environment can potentially improve the
intra-procedural tumor targeting, which will significantly improve therapeutic
outcomes. However, the intra-procedural CBCT often suffers from suboptimal
image quality due to lack of signal calibration for Hounsfield unit, limited
FOV, and motion/metal artifacts. These non-ideal conditions make standard
intensity-based multimodal registration methods infeasible to generate correct
transformation across modalities. While registration based on anatomic
structures, such as segmentation or landmarks, provides an efficient
alternative, such anatomic structure information is not always available. One
can train a deep learning-based anatomy extractor, but it requires large-scale
manual annotations on specific modalities, which are often extremely
time-consuming to obtain and require expert radiological readers. To tackle
these issues, we leverage annotated datasets already existing in a source
modality and propose an anatomy-preserving domain adaptation to segmentation
network (APA2Seg-Net) for learning segmentation without target modality ground
truth. The segmenters are then integrated into our anatomy-guided multimodal
registration based on the robust point matching machine. Our experimental
results on in-house TACE patient data demonstrated that our APA2Seg-Net can
generate robust CBCT and MR liver segmentation, and the anatomy-guided
registration framework with these segmenters can provide high-quality
multimodal registrations. Our code is available at
https://github.com/bbbbbbzhou/APA2Seg-Net.
