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.

通过利用计算机断层扫描（CT）和磁共振成像（MRI）多模态图像的信息特征，我们提出了一种初步的空间对齐算法和弱监督多模态注册网络，有效地实现了不同深度的多模态图像的自动对齐，通过金字塔特征和代价体积来估计光流，并在多种评估指标上证明了该方法在传统体积渲染对齐方法和注册网络方面的优越性，从而证明了我们模型在多模态图像注册中的有效性。

MR-CT 图像弱监督下的颈部宫颈癌放射治疗对齐与配准

Weakly supervised alignment and registration of MR-CT for cervical  cancer radiotherapy

Purpose: This paper presents the preliminary results of a semi-automatic
method for prostate segmentation of Magnetic Resonance Images (MRI) which aims
to be incorporated in a navigation system for prostate brachytherapy. Methods:
The method is based on the registration of an anatomical atlas computed from a
population of 18 MRI exams onto a patient image. An hybrid registration
framework which couples an intensity-based registration with a robust
point-matching algorithm is used for both atlas building and atlas
registration. Results: The method has been validated on the same dataset that
the one used to construct the atlas using the "leave-one-out method". Results
gives a mean error of 3.39 mm and a standard deviation of 1.95 mm with respect
to expert segmentations. Conclusions: We think that this segmentation tool may
be a very valuable help to the clinician for routine quantitative image
exploitation.

本文介绍了一种半自动半自动前列腺磁共振图像分割方法的初步结果，旨在将其纳入前列腺放射性治疗的导航系统中。该方法基于由 18 个磁共振检查组成的人群计算的解剖图谱的配准，并采用一种混合配准框架，将基于强度的配准与强大的点匹配算法相结合，用于解剖图谱的构建和配准。在与专家分割相比的验证中，该方法的平均误差为 3.39 毫米，标准差为 1.95 毫米。我们认为，这种分割工具可能对临床医生的常规定量图像分析非常有帮助。