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 Oguz; Alison Pouch; Nadav Schwartz; Brett C Byram; Ipek Oguz

arXiv · 2411.06750

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 Oguz·Alison Pouch·Nadav Schwartz·Brett C Byram·Ipek Oguz

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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. 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.

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eess.IV cs.CV

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Xing Yao, Runxuan Yu, Dewei Hu, Hao Yang, Ange Lou, Jiacheng Wang, Daiwei Lu, Gabriel Arenas, Baris Oguz, Alison Pouch, Nadav Schwartz, Brett C Byram, Ipek Oguz. 2024-11-11. SynStitch: a Self-Supervised Learning Network for Ultrasound Image Stitching Using Synthetic Training Pairs and Indirect Supervision. https://arxiv.org/abs/2411.06750

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