用合成数据自监督训练，提升超声图像拼接精度
SynStitch: a Self-Supervised Learning Network for Ultrasound Image Stitching Using Synthetic Training Pairs and Indirect Supervision
- 通过控制网络生成带已知变换的合成图像对
- 在肾脏超声数据集上优于多个主流方法
- 适合缺乏标注数据的医学图像拼接任务
超声图像拼接可通过整合不同探头位置的多幅图像扩大视野。然而，在解剖结构仅部分重叠的情况下进行图像配准极具挑战。本文提出SynStitch，一种用于2D超声图像拼接的自监督框架，包含合成拼接对生成模块（SSPGM）和图像拼接模块（ISM）。SSPGM利用基于补丁的ControlNet，从单张输入图像生成具有已知仿射变换矩阵的真实感2D超声拼接对。ISM则利用这些合成成对数据以监督方式学习2D超声拼接。该框架在肾脏超声数据集上与多个领先方法对比，通过定性和定量分析均表现出更优的拼接性能。代码将在论文接收后公开。
原文摘要 · Abstract (English)
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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