Islam Mahdi, Tabassum Musarrat, Mayr Agnes, Kremser Christian, Haltmeier Markus, Almar-Munoz Enrique
Department of Radiology, Medical University of Innsbruck, 6020 Innsbruck, Austria.
Department of Mathematics, University of Innsbruck, 6020 Innsbruck, Austria.
J Imaging. 2025 Sep 17;11(9):318. doi: 10.3390/jimaging11090318.
Transcatheter aortic valve implantation (TAVI) is a minimally invasive procedure for treating severe aortic stenosis, where optimal vascular access route selection is critical to reduce complications. It requires careful selection of the iliac artery with the most favourable anatomy, specifically, one with the largest diameters and no segments narrower than 5 mm. This process is time-consuming when carried out manually. We present an active learning-based segmentation framework for contrast-enhanced Cardiac Magnetic Resonance (CMR) data, guided by probabilistic uncertainty and pseudo-labelling, enabling efficient segmentation with minimal manual annotation. The segmentations are then fed into an automated pipeline for diameter quantification, achieving a Dice score of 0.912 and a mean absolute percentage error (MAPE) of 4.92%. An ablation study using pre- and post-contrast CMR showed superior performance with post-contrast data only. Overall, the pipeline provides accurate segmentation and detailed diameter profiles of the aorto-iliac route, helping the assessment of the access route.
经导管主动脉瓣植入术（TAVI）是一种治疗严重主动脉瓣狭窄的微创手术，其中选择最佳的血管入路对于减少并发症至关重要。这需要仔细选择解剖结构最有利的髂动脉，具体来说，是直径最大且无窄于5毫米节段的髂动脉。手动进行此过程很耗时。我们提出了一种基于主动学习的分割框架，用于对比增强心脏磁共振（CMR）数据，由概率不确定性和伪标记引导，能够以最少的手动标注实现高效分割。然后将分割结果输入到用于直径量化的自动化管道中，Dice评分为0.912，平均绝对百分比误差（MAPE）为4.92%。使用对比前和对比后CMR的消融研究表明，仅使用对比后数据时性能更优。总体而言，该管道提供了主动脉 - 髂动脉路径的准确分割和详细直径轮廓，有助于评估入路。
