标题： 显著区域匹配用于完全自动的MR-TRUS配准 显示英文标题

标题： Salient Region Matching for Fully Automated MR-TRUS Registration

摘要： 前列腺癌是男性癌症相关死亡的主要原因。 磁共振（MR）和经直肠超声（TRUS）的配准可以为前列腺癌的靶向活检提供指导。 在本研究中，我们提出了一种显著区域匹配框架，用于完全自动化的MR-TRUS配准。 该框架包括前列腺分割、刚性对齐和非刚性配准。 前列腺分割分别在MR和TRUS上使用两个分割网络进行，预测的显著区域用于刚性对齐。 刚性对齐后的MR和TRUS图像作为非刚性配准的初始化。 非刚性配准网络具有双流编码器，包含跨模态空间注意力模块以促进多模态特征学习，并具有显著区域匹配损失，以考虑前列腺区域内结构和强度的相似性。 在公开的MR-TRUS数据集上的实验表明，我们的方法实现了令人满意的配准结果，优于几种先进的方法。 代码可在 https://github.com/mock1ngbrd/salient-region-matching 公开获取。 显示英文摘要

摘要： Prostate cancer is a leading cause of cancer-related mortality in men. The registration of magnetic resonance (MR) and transrectal ultrasound (TRUS) can provide guidance for the targeted biopsy of prostate cancer. In this study, we propose a salient region matching framework for fully automated MR-TRUS registration. The framework consists of prostate segmentation, rigid alignment and deformable registration. Prostate segmentation is performed using two segmentation networks on MR and TRUS respectively, and the predicted salient regions are used for the rigid alignment. The rigidly-aligned MR and TRUS images serve as initialization for the deformable registration. The deformable registration network has a dual-stream encoder with cross-modal spatial attention modules to facilitate multi-modality feature learning, and a salient region matching loss to consider both structure and intensity similarity within the prostate region. Experiments on a public MR-TRUS dataset demonstrate that our method achieves satisfactory registration results, outperforming several cutting-edge methods. The code is publicly available at https://github.com/mock1ngbrd/salient-region-matching.