用深度学习自动对齐血管超声与光学成像，速度快且精准。
A novel framework for fully-automated co-registration of intravascular ultrasound and optical coherence tomography imaging data
- 基于深度学习自动提取血管边界和钙化等特征，驱动图像对齐。
- 纵向对齐相关性超0.99，横向对齐相关性超0.90，媲美专家水平。
- 处理单根血管<90秒，适合大规模多模态斑块研究。
旨在开发一种深度学习框架，实现血管内超声（IVUS）与光学相干断层扫描（OCT）图像的全自动纵向和环向配准。分析了230名患者（714根血管）在非罪犯血管中接受近红外光谱-IVUS与OCT成像的数据。利用61,655帧NIRS-IVUS和62,334帧OCT中专家标注的管腔边界，以及10,000帧NIRS-IVUS和10,000帧OCT中的分支和钙化组织数据，训练深度学习模型以自动提取这些特征。训练后的模型处理NIRS-IVUS与OCT图像后，由动态时间规整算法进行纵向配准，环向配准则通过动态规划优化。在22名患者共77根血管的测试集中，该方法在纵向和环向配准上与专家一致率均极高（纵向一致性相关系数>0.99，环向>0.90），威廉斯指数分别为0.96和0.97，性能接近人工分析。整个流程处理单根血管耗时不足90秒。结论：该基于深度学习的全自动配准框架快速准确，适用于大规模多模态成像研究中斑块成分的分析。
原文摘要 · Abstract (English)
Aims: To develop a deep-learning (DL) framework that will allow fully automated longitudinal and circumferential co-registration of intravascular ultrasound (IVUS) and optical coherence tomography (OCT) images. Methods and results: Data from 230 patients (714 vessels) with acute coronary syndrome that underwent near-infrared spectroscopy (NIRS)-IVUS and OCT imaging in their non-culprit vessels were included in the present analysis. The lumen borders annotated by expert analysts in 61,655 NIRS-IVUS and 62,334 OCT frames, and the side branches and calcific tissue identified in 10,000 NIRS-IVUS frames and 10,000 OCT frames, were used to train DL solutions for the automated extraction of these features. The trained DL solutions were used to process NIRS-IVUS and OCT images and their output was used by a dynamic time warping algorithm to co-register longitudinally the NIRS-IVUS and OCT images, while the circumferential registration of the IVUS and OCT was optimized through dynamic programming. On a test set of 77 vessels from 22 patients, the DL method showed high concordance with the expert analysts for the longitudinal and circumferential co-registration of the two imaging sets (concordance correlation coefficient >0.99 for the longitudinal and >0.90 for the circumferential co-registration). The Williams Index was 0.96 for longitudinal and 0.97 for circumferential co-registration, indicating a comparable performance to the analysts. The time needed for the DL pipeline to process imaging data from a vessel was <90s. Conclusion: The fully automated, DL-based framework introduced in this study for the co-registration of IVUS and OCT is fast and provides estimations that compare favorably to the expert analysts. These features renders it useful in research in the analysis of large-scale data collected in studies that incorporate multimodality imaging to characterize plaque composition.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。