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.

翻译：在脊柱侧弯手术中，C型臂X光机的有限视野限制了外科医生对脊柱结构的整体分析。本文提出了一种用于脊柱侧弯手术的端到端高效且鲁棒的术中X光图像拼接方法，命名为SX-Stitch。该方法分为两个阶段：分割与拼接。在分割阶段，我们提出了一种名为Vision Mamba of Spine-UNet（VMS-UNet）的医学图像分割模型，该模型利用状态空间Mamba捕获长距离上下文信息，同时保持线性计算复杂度，并融合了SimAM注意力机制，显著提升了分割性能。在拼接阶段，我们将图像间的对齐过程简化为一个配准能量函数的最小化问题。随后，通过优化总能量函数对无序图像进行排序，并引入混合能量函数来优化最佳接缝，有效消除了视差伪影。在临床数据集上，SX-Stitch在定性和定量评估中均展现出优于当前最优（SOTA）方案的性能。