Ultrasound (US) image stitching can expand the field-of-view (FOV) by combining multiple US images from varied probe positions. However, registering US images with only partially overlapping anatomical contents is a challenging task. In this work, we introduce SynStitch, a self-supervised framework designed for 2DUS stitching. SynStitch consists of a synthetic stitching pair generation module (SSPGM) and an image stitching module (ISM). SSPGM utilizes a patch-conditioned ControlNet to generate realistic 2DUS stitching pairs with known affine matrix from a single input image. ISM then utilizes this synthetic paired data to learn 2DUS stitching in a supervised manner. Our framework was evaluated against multiple leading methods on a kidney ultrasound dataset, demonstrating superior 2DUS stitching performance through both qualitative and quantitative analyses. The code will be made public upon acceptance of the paper.

翻译：超声（US）图像拼接能够通过融合来自不同探头位置的多幅超声图像来扩展视野（FOV）。然而，对仅部分重叠解剖内容的超声图像进行配准是一项具有挑战性的任务。在本工作中，我们提出了SynStitch，一个为二维超声图像拼接设计的自监督框架。SynStitch包含一个合成拼接对生成模块（SSPGM）和一个图像拼接模块（ISM）。SSPGM利用基于图像块的ControlNet，从单张输入图像生成具有已知仿射矩阵的真实二维超声拼接对。ISM随后利用这些合成的配对数据，以监督学习的方式学习二维超声图像拼接。我们在肾脏超声数据集上将我们的框架与多种领先方法进行了对比评估，通过定性和定量分析均证明了其卓越的二维超声图像拼接性能。代码将在论文被接受后公开。