通过显著区域匹配实现全自动前列腺MRI与超声图像配准
Salient Region Matching for Fully Automated MR-TRUS Registration
- 用双模态分割网络提取显著区域，指导刚性对齐
- 引入跨模态注意力与显著区匹配损失，提升配准精度
- 适合医学图像配准研究者及临床辅助诊断开发人员
前列腺癌是男性癌症相关死亡的主要原因。磁共振（MR）与经直肠超声（TRUS）图像的配准可为前列腺靶向活检提供引导。本文提出一种全自动的显著区域匹配框架，包含前列腺分割、刚性对齐和非刚性配准三阶段。在MR和TRUS图像上分别使用两个分割网络进行前列腺分割，提取出的显著区域用于刚性对齐；刚性对齐后的图像作为非刚性配准的初始输入。非刚性配准网络采用双流编码器结构，结合跨模态空间注意力模块以促进多模态特征学习，并引入显著区域匹配损失，兼顾前列腺区域内结构与强度相似性。在公开的MR-TRUS数据集上的实验表明，该方法取得了满意的配准效果，优于多个前沿方法。代码已开源：https://github.com/mock1ngbrd/salient-region-matching。
原文摘要 · Abstract (English)
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.
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