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.

翻译：宫颈癌是导致女性死亡的主要原因之一，而近距离放疗是目前的主要治疗手段。然而，精确界定宫颈旁组织浸润范围对于改善癌症诊断和治疗方案至关重要。融合计算机断层扫描（CT）与磁共振成像（MRI）两种模态的信息特征，有助于实现宫颈旁组织浸润范围的精确勾画。配准是信息融合的首要步骤。然而，在对不同深度的多模态图像进行对齐时，手动对齐不仅容易产生较大误差，且耗时费力。此外，多模态图像感兴趣区域（ROI）尺寸和形状的差异对实现精确配准构成了重大挑战。本文提出了一种初步的空间对齐算法和一种弱监督多模态配准网络。该空间位置对齐算法可高效利用医生提供的两种模态图像中的有限标注信息，自动对齐不同深度的多模态图像。通过利用对齐后的多模态图像进行弱监督配准，并结合金字塔特征与代价体进行光流估计，实验结果表明，所提方法在各项评估指标上均优于传统的体绘制对齐方法与配准网络，验证了该模型在多模态图像配准中的有效性。