Cascaded learning in intravascular ultrasound: coronary stent delineation in manual pullbacks.

Autor:	Wissel T; Philips Research, Hamburg, Germany., Riedl KA; University Heart and Vascular Center Hamburg, Department of Cardiology, Hamburg, Germany., Schaefers K; Philips Research, Eindhoven, The Netherlands., Nickisch H; Philips Research, Hamburg, Germany., Brunner FJ; University Heart and Vascular Center Hamburg, Department of Cardiology, Hamburg, Germany., Schnellbaecher ND; Philips Research, Hamburg, Germany., Blankenberg S; University Heart and Vascular Center Hamburg, Department of Cardiology, Hamburg, Germany.; German Center for Cardiovascular Research (DZHK), Partner Site Hamburg/Lübeck/Kiel, Germany., Seiffert M; University Heart and Vascular Center Hamburg, Department of Cardiology, Hamburg, Germany.; German Center for Cardiovascular Research (DZHK), Partner Site Hamburg/Lübeck/Kiel, Germany., Grass M; Philips Research, Hamburg, Germany.
Jazyk:	angličtina
Zdroj:	Journal of medical imaging (Bellingham, Wash.) [J Med Imaging (Bellingham)] 2022 Mar; Vol. 9 (2), pp. 025001. Date of Electronic Publication: 2022 Mar 28.
DOI:	10.1117/1.JMI.9.2.025001
Abstrakt:	Purpose: Implanting stents to re-open stenotic lesions during percutaneous coronary interventions is considered a standard treatment for acute or chronic coronary syndrome. Intravascular ultrasound (IVUS) can be used to guide and assess the technical success of these interventions. Automatically segmenting stent struts in IVUS sequences improves workflow efficiency but is non-trivial due to a challenging image appearance entailing manifold ambiguities with other structures. Manual, ungated IVUS pullbacks constitute a challenge in this context. We propose a fully data-driven strategy to first longitudinally detect and subsequently segment stent struts in IVUS frames. Approach: A cascaded deep learning approach is presented. It first trains an encoder model to classify frames as "stent," "no stent," or "no use." A segmentation model then delineates stent struts on a pixel level only in frames with a stent label. The first stage of the cascade acts as a gateway to reduce the risk for false positives in the second stage, the segmentation, which is trained on a smaller and difficult-to-annotate dataset. Training of the classification and segmentation model was based on 49,888 and 1826 frames of 74 sequences from 35 patients, respectively. Results: The longitudinal classification yielded Dice scores of 92.96%, 82.35%, and 94.03% for the classes stent, no stent, and no use, respectively. The segmentation achieved a Dice score of 65.1% on the stent ground truth (intra-observer performance: 75.5%) and 43.5% on all frames (including frames without stent, with guidewires, calcium, or without clinical use). The latter improved to 49.5% when gating the frames by the classification decision and further increased to 57.4% with a heuristic on the plausible stent strut area. Conclusions: A data-driven strategy for segmenting stents in ungated, manual pullbacks was presented-the most common and practical scenario in the time-critical clinical workflow. We demonstrated a mitigated risk for ambiguities and false positive predictions. (© 2022 Society of Photo-Optical Instrumentation Engineers (SPIE).)
Databáze:	MEDLINE
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