SynStitch: A Self-Supervised Learning Network for Ultrasound Image Stitching Using Synthetic Training Pairs and Indirect Supervision

Xing Yao, Runxuan Yu, Dewei Hu, Hao Yang, Ange Lou, Jiacheng Wang, Daiwei Lu, Gabriel Arenas, Baris U. Oguz, Alison M. Pouch, Nadav Schwartz, Brett C. Byram, Ipek Oguz

Abstract: 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. Syn-Stitch 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 is available at https://github.com/MedICL-VU/SynStitch.