SX-Stitch: An Efficient VMS-UNet Based Framework for Intraoperative Scoliosis X-Ray Image Stitching

TL;DR

SX-Stitch is a novel framework combining a specialized segmentation model and an optimized stitching process to improve intraoperative scoliosis X-ray image assembly, enhancing surgical visualization.

Contribution

The paper introduces VMS-UNet with Mamba and SimAM for improved segmentation, and a hybrid energy-based stitching method for better image alignment and artifact removal.

Findings

Outperforms state-of-the-art methods qualitatively and quantitatively

Effective in eliminating parallax artifacts

Provides a robust solution for intraoperative image stitching

Abstract

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.