用新模型拼接脊柱侧弯手术中碎片化X光片，提升术中影像清晰度。
SX-Stitch: An Efficient VMS-UNet Based Framework for Intraoperative Scoliosis X-Ray Image Stitching
- 基于VMS-UNet分割脊柱图像，融合Mamba与SimAM提升长程上下文感知能力。
- 通过能量函数优化实现无序图像自动对齐，有效消除视差伪影。
- 在临床数据集上优于现有方法，适合术中实时拼接需求。
在脊柱侧弯手术中，C臂X射线机视野有限，限制了医生对脊柱结构的全面评估。本文提出一种端到端高效且鲁棒的术中X射线图像拼接方法SX-Stitch，分为分割与拼接两阶段。分割阶段提出视觉脊柱UNet（VMS-UNet）模型，利用状态空间模型Mamba捕获长距离上下文信息，同时保持线性计算复杂度，并引入SimAM注意力机制，显著提升分割性能。拼接阶段将图像对齐简化为注册能量函数最小化问题，通过混合能量函数优化最佳拼接缝，有效消除视差伪影。在临床数据集上，SX-Stitch在定性和定量上均优于当前最优方法。
原文摘要 · Abstract (English)
In scoliosis surgery, the limited field of view of the C-arm X-ray machine restricts the surgeons' holistic analysis of spinal structures .This paper presents an end-to-end efficient and robust intraoperative X-ray image stitching method for scoliosis surgery,named SX-Stitch. The method is divided into two stages:segmentation and stitching. In the segmentation stage, We propose a medical image segmentation model named Vision Mamba of Spine-UNet (VMS-UNet), which utilizes the state space Mamba to capture long-distance contextual information while maintaining linear computational complexity, and incorporates the SimAM attention mechanism, significantly improving the segmentation performance.In the stitching stage, we simplify the alignment process between images to the minimization of a registration energy function. The total energy function is then optimized to order unordered images, and a hybrid energy function is introduced to optimize the best seam, effectively eliminating parallax artifacts. On the clinical dataset, Sx-Stitch demonstrates superiority over SOTA schemes both qualitatively and quantitatively.
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