Registration and fusion of magnetic resonance imaging (MRI) and transrectal ultrasound (TRUS) of the prostate can provide guidance for prostate brachytherapy. However, accurate registration remains a challenging task due to the lack of ground truth regarding voxel-level spatial correspondence, limited field of view, low contrast-to-noise ratio, and signal-to-noise ratio in TRUS. In this study, we proposed a fully automated deep learning approach based on a weakly supervised method to address these issues. We employed deep learning techniques to combine image segmentation and registration, including affine and nonrigid registration, to perform an automated deformable MRI-TRUS registration. To start with, we trained two separate fully convolutional neural networks (CNNs) to perform a pixel-wise prediction for MRI and TRUS prostate segmentation. Then, to provide the initialization of the registration, a 2D CNN was used to register MRI-TRUS prostate images using an affine registrati...

中文翻译：

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标签驱动磁共振成像（MRI）-经直肠超声（TRUS）配准使用弱指导学习进行MRI引导的前列腺放射治疗

前列腺的磁共振成像（MRI）和经直肠超声（TRUS）的配准和融合可以为前列腺近距离治疗提供指导。但是，由于在TRUS中缺乏关于体素级别的空间对应性，有限的视野，低的对噪比和信噪比等方面的事实，准确的配准仍然是一项艰巨的任务。在这项研究中，我们提出了一种基于弱监督方法的全自动深度学习方法，以解决这些问题。我们采用深度学习技术将图像分割和配准（包括仿射和非刚性配准）相结合，以执行自动可变形MRI-TRUS配准。首先，我们训练了两个单独的全卷积神经网络（CNN），以对MRI和TRUS前列腺分割进行像素级预测。然后，