Volpi Daniele, Sarhan Mhd H, Ghotbi Reza, Navab Nassir, Mateus Diana, Demirci Stefanie
Computer Aided Medical Procedures, Technische Universität München, Boltzmannstr 3, 85748, Garching, Germany.
Int J Comput Assist Radiol Surg. 2015 Jun;10(6):773-81. doi: 10.1007/s11548-015-1217-y. Epub 2015 May 16.
The continuous integration of innovative imaging modalities into conventional vascular surgery rooms has led to an urgent need for computer assistance solutions that support the smooth integration of imaging within the surgical workflow. In particular, endovascular interventions performed under 2D fluoroscopic or angiographic imaging only, require reliable and fast navigation support for complex treatment procedures such as endovascular aortic repair. Despite the vast variety of image-based guide wire and catheter tracking methods, an adoption of these for detecting and tracking the stent graft delivery device is not possible due to its special geometry and intensity appearance.
In this paper, we present, for the first time, the automatic detection and tracking of the stent graft delivery device in 2D fluoroscopic sequences on the fly. The proposed approach is based on the robust principal component analysis and extends the conventional batch processing towards an online tracking system that is able to detect and track medical devices on the fly.
The proposed method has been tested on interventional sequences of four different clinical cases. In the lack of publicly available ground truth data, we have further initiated a crowd sourcing strategy that has resulted in 200 annotations by unexperienced users, 120 of which were used to establish a ground truth dataset for quantitatively evaluating our algorithm. In addition, we have performed a user study amongst our clinical partners for qualitative evaluation of the results.
Although we calculated an average error in the range of nine pixels, the fact that our tracking method functions on the fly and is able to detect stent grafts in all unfolding stages without fine-tuning of parameters has convinced our clinical partners and they all agreed on the very high clinical relevance of our method.
将创新成像模式持续整合到传统血管外科手术室中，迫切需要计算机辅助解决方案来支持成像在手术工作流程中的顺利整合。特别是，仅在二维荧光透视或血管造影成像下进行的血管内介入手术，对于诸如血管内主动脉修复等复杂治疗程序，需要可靠且快速的导航支持。尽管基于图像的导丝和导管跟踪方法种类繁多，但由于支架移植物输送装置的特殊几何形状和强度外观，无法采用这些方法来检测和跟踪该装置。
在本文中，我们首次展示了在二维荧光透视序列中实时自动检测和跟踪支架移植物输送装置的方法。所提出的方法基于稳健主成分分析，并将传统的批处理扩展为一个能够实时检测和跟踪医疗设备的在线跟踪系统。
所提出的方法已在四个不同临床病例的介入序列上进行了测试。由于缺乏公开可用的地面真值数据，我们进一步启动了众包策略，未经验的用户提供了200个标注，其中120个用于建立地面真值数据集以定量评估我们的算法。此外，我们在临床合作伙伴中进行了用户研究以对结果进行定性评估。
尽管我们计算出的平均误差在9像素范围内，但我们的跟踪方法能够实时运行且无需微调参数就能在所有展开阶段检测到支架移植物，这一事实说服了我们的临床合作伙伴，他们都认同我们方法具有很高的临床相关性。
