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Ischemic heart disease remains one of the leading causes of death worldwide. Percutaneous coronary interventions (PCIs) for implanting coronary stents are preferred for patients with acute myocardial infarction but may also be performed in patients with chronic coronary syndromes to improve symptoms and outcome. During the PCI, the assessment of stent apposition, evaluation of in-stent restenosis or guidance for complex stenting of bifurcation lesions may be improved by intravascular imaging such as intravascular ultrasound (IVUS). However, advanced interpretation of the image often requires expertise and training. To approach this issue, we introduce an automatic delineation of stent struts within the IVUS pullback. We propose a cascaded segmentation based on data-driven learning with a neural encoder-decoder architecture. The learning process uses 80 IVUS sequences from 28 patients which were acquired and partially annotated by the Department of Cardiology, University Heart and Vascular Center Hamburg, Germany. The annotations include 1108, 555 and 355 frames with delineated lumen, stent and calcium as well as 13696 and 10689 frame-wise stent and no-stent indications. The network was pre-trained on lumen segmentation and refined to first identify stent frames using an encoder network and subsequently segment the struts with a decoder. Quantitative evaluation using 3-fold cross-validation revealed 88.3% precision, 92.4% recall and 0.824 Dice for the encoder and 67.0%, 60.3% and 0.611 for the decoder. We conclude that the encoder successfully leverages the larger number of high-level annotations to reject non-stent frames avoiding unnecessary false positives for the decoder trained on much less, but fine-granular annotations.
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