用AI自动去除脊柱手术影像伪影，提升测量准确性。
ARNAI: Artifact Removal Network based on Autoencoding and Inpainting for Robust Spinal Image Segmentation and Measurement
- 设计自编码+修复网络ARNAI，消除植入物造成的影像干扰。
- 对L4-L5节段弯度角误差降低70%，测量一致性显著提升。
- 适合需要精准评估术后脊柱参数的临床医生和研究者。
目的：开发一种适用于术后影像的AI框架，实现含脊柱植入物的侧位腰椎X光片中骨盆-脊柱参数的自动化测量，具备对植入物伪影的鲁棒性。方法：回顾两家机构的腰椎侧位X光片数据（内部：2017年1月–2024年12月；外部：2021年10月–2025年9月）。提出Restore, Segment, and Measure（RSM）框架，集成新型自编码与修复结合的伪影去除网络ARNAI，以减轻术后影像中植入物引起的伪影。通过威尔科克斯符号秩检验和组内相关系数（ICC）评估分割及骨盆-脊柱参数（PT、LL、SS、SCA）测量性能。结果：将ARNAI引入基于Transformer的分割模型FCBFormer后，平均Dice相似系数从0.814提升至0.870，其中L3–L5段提升明显，L1–L2段改善较小。在91张含植入物的图像中，L4–L5节段弯曲角误差由15.6–16.2°降至4.7°，平均误差减少70%。L4–L5节段弯曲角的ICC分别提升至0.54（评分者1）和0.59（评分者2），从0.18显著提高；骨盆倾斜角、腰椎前凸角和骶骨倾斜角的ICC均超过0.70。在经多重比较校正后的内部含植入物队列中，该改进具有统计学意义。结论：所提出的RSM框架显著提升了含植入物术后影像中骨盆-脊柱参数的自动化测量性能。通过抑制植入物相关伪影，ARNAI有效提升了分割与下游测量的准确性，尤其在L4–L5节段弯曲角估计中效果最显著，平均误差降低约70%。
原文摘要 · Abstract (English)
Purpose: This study aims to develop an AI framework applicable for postoperative imaging for automated measurement of spinopelvic parameters on radiographs with robustness to the presence of spinal implants. Materials and Methods: We retrospectively reviewed lateral lumbar spine radiographs from two institutions (Internal: January 2017--December 2024; External: October 2021--September 2025). We developed the Restore, Segment, and Measure (RSM) framework, incorporating a novel Artifact Removal Network based on Autoencoding and Inpainting (ARNAI) to mitigate implant-related artifacts in postoperative radiographs. Segmentation and spinopelvic parameter (PT, LL, SS, SCA) measurement performance were assessed using Wilcoxon signed-rank tests and intraclass correlation coefficients. Results: When ARNAI was added to a recent Transformer-based segmentation model, FCBFormer, the mean DSC increased to 0.870 from 0.814, with marked gains at L3--L5 and smaller improvements at L1--L2. On 91 radiographs with implants, the mean L4--L5 segmental Cobb angle error decreased to 4.7 {\deg} from 15.6--16.2 {\deg}, an average error reduction of 70%. The ICC for L4--L5 segmental Cobb angle improved to 0.54 (Rater 1) and 0.59 (Rater 2) from 0.18, and ICCs for pelvic tilt, lumbar lordosis, and sacral slope all exceeded 0.70. The improvement in L4--L5 segmental Cobb angle error was statistically significant in the internal implant-containing cohort after correction for multiple comparisons. Conclusion: The proposed RSM framework improved automated spinopelvic parameter measurement in implant-containing postoperative radiographs. By mitigating implant-related artifacts, ARNAI improved segmentation and downstream measurement accuracy, with the greatest benefit observed for L4--L5 segmental Cobb angle estimation, where the mean error was reduced by approximately 70%.
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