Mistelbauer, G., Morar, A., Schernthaner, R., Strassl, A., Fleischmann, D., Moldoveanu, F., & Gröller, M. E. (2021). Semi-automatic vessel detection for challenging cases of peripheral arterial disease. Computers in Biology and Medicine, 133(104344), 104344. https://doi.org/10.1016/j.compbiomed.2021.104344
Objectives: Manual or semi-automated segmentation of the lower extremity arterial tree in patients with Pe-ripheral arterial disease (PAD) remains a notoriously difﬁcult and time-consuming task. The complex manifes-tations of the disease, including discontinuities of the vascular ﬂow channels, the presence of calciﬁed atherosclerotic plaque in close vicinity to adjacent bone, and the presence of metal or other imaging artifacts currently preclude fully automated vessel identiﬁcation. New machine learning techniques may alleviate this challenge, but require large and reasonably well segmented training data. Methods: We propose a novel semi-automatic vessel tracking approach for peripheral arteries to facilitate and accelerate the creation of annotated training data by expert cardiovascular radiologists or technologists, while limiting the number of necessary manual interactions, and reducing processing time. After automatically clas-sifying blood vessels, bones, and other tissue, the relevant vessels are tracked and organized in a tree-like structure for further visualization. Results: We conducted a pilot (N = 9) and a clinical study (N = 24) in which we assess the accuracy and required time for our approach to achieve sufﬁcient quality for clinical application, with our current clinically established workﬂow as the standard of reference. Our approach enabled expert physicians to readily identify all clinically relevant lower extremity arteries, even in problematic cases, with an average sensitivity of 92.9%, and an average speciﬁcity and overall accuracy of 99.9%. Conclusions: Compared to the clinical workﬂow in our collaborating hospitals (28:40 ± 7:45 [mm:ss]), our approach (17:24 ± 6:44 [mm:ss]) is on average 11:16 [mm:ss] (39%) faster.
Visual Computing and Human-Centered Technology: 100%