用深度学习实现冠脉支架三维贴壁评估，精准定位支架与血管距离。
3D Distance-color-coded Assessment of PCI Stent Apposition via Deep-learning-based Three-dimensional Multi-object Segmentation
- 基于3D多目标分割技术，自动识别血管腔和支架
- 分割精度超95%，将支架-血管距离映射为彩色3D图
- 适合心血管介入医生做支架植入后评估与个性化治疗
冠状动脉疾病是全球重大健康挑战，常需经皮冠状动脉介入（PCI）植入支架。评估支架贴壁情况对预防和发现支架内再狭窄等并发症至关重要。本文提出一种基于深度学习的三维多目标分割方法，实现血管内光学相干断层成像（IV-OCT）中支架与血管腔的三维精确分割，并构建三维距离-颜色编码评估系统（3D DccA）。该方法采用空间匹配网络与风格迁移双重训练策略，准确量化并可视化支架与血管腔之间的三维距离分布。分割精度超过95%，显著提升临床对支架植入质量的评估能力，支持个性化治疗方案制定。
原文摘要 · Abstract (English)
Coronary artery disease poses a significant global health challenge, often necessitating percutaneous coronary intervention (PCI) with stent implantation. Assessing stent apposition holds pivotal importance in averting and identifying PCI complications that lead to in-stent restenosis. Here we proposed a novel three-dimensional (3D) distance-color-coded assessment (DccA)for PCI stent apposition via deep-learning-based 3D multi-object segmentation in intravascular optical coherence tomography (IV-OCT). Our proposed 3D DccA accurately segments 3D vessel lumens and stents in IV-OCT images, using a spatial matching network and dual-layer training with style transfer. It quantifies and maps stent-lumen distances into a 3D color space, facilitating 3D visual assessment of PCI stent apposition. Achieving over 95% segmentation precision, our proposed DccA enhances clinical evaluation of PCI stent deployment and supports personalized treatment planning.
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。