  Cervical cancer is one of the leading causes of death in women, and
brachytherapy is currently the primary treatment method. However, it is
important to precisely define the extent of paracervical tissue invasion to
improve cancer diagnosis and treatment options. The fusion of the information
characteristics of both computed tomography (CT) and magnetic resonance
imaging(MRI) modalities may be useful in achieving a precise outline of the
extent of paracervical tissue invasion. Registration is the initial step in
information fusion. However, when aligning multimodal images with varying
depths, manual alignment is prone to large errors and is time-consuming.
Furthermore, the variations in the size of the Region of Interest (ROI) and the
shape of multimodal images pose a significant challenge for achieving accurate
registration.In this paper, we propose a preliminary spatial alignment
algorithm and a weakly supervised multimodal registration network. The spatial
position alignment algorithm efficiently utilizes the limited annotation
information in the two modal images provided by the doctor to automatically
align multimodal images with varying depths. By utilizing aligned multimodal
images for weakly supervised registration and incorporating pyramidal features
and cost volume to estimate the optical flow, the results indicate that the
proposed method outperforms traditional volume rendering alignment methods and
registration networks in various evaluation metrics. This demonstrates the
effectiveness of our model in multimodal image registration.
