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

TL;DR

SynStitch is a novel self-supervised learning framework that uses synthetic training pairs and indirect supervision to improve ultrasound image stitching, expanding the field-of-view in medical imaging.

Contribution

The paper introduces SynStitch, combining synthetic pair generation and supervised learning for ultrasound stitching, addressing challenges with partially overlapping images.

Findings

Outperforms existing methods in qualitative assessments

Achieves higher quantitative stitching accuracy

Demonstrates robustness across different ultrasound images

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.