Dr. Fabian J. Brunner Profile

Dr. Fabian J. Brunner

at Univ. Herz- und Gefäßzentrum Hamburg

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Author

Publications (3)

SPIE Journal Paper | 28 March 2022

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

Tobias Wissel, Katharina Riedl, Klaus Schaefers, Hannes Nickisch, Fabian Brunner, Nikolas Schnellbächer, Stefan Blankenberg, Moritz Seiffert, Michael Grass

JMI, Vol. 9, Issue 02, 025001, (March 2022) https://doi.org/10.1117/12.10.1117/1.JMI.9.2.025001

KEYWORDS: Intravascular ultrasound, Image segmentation, Sensors, Computer programming, Calcium, Transducers, Speckle, Image resolution, Data modeling, Monte Carlo methods

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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.

Proceedings Article | 15 February 2021 Presentation + Paper

Tailored methods for segmentation of intravascular ultrasound images via convolutional neural networks

Lennart Bargsten, Katharina Riedl, Tobias Wissel, Fabian Brunner, Klaus Schaefers, Johanna Sprenger, Michael Grass, Moritz Seiffert, Stefan Blankenberg, Alexander Schlaefer

Proceedings Volume 11602, 1160204 (2021) https://doi.org/10.1117/12.2580720

KEYWORDS: Image segmentation, Intravascular ultrasound, Convolutional neural networks, Independent component analysis, Arteries, Speckle, Shape analysis, Optical coherence tomography, Error analysis

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Proceedings Article | 15 February 2021 Presentation + Paper

Delineation of coronary stents in intravascular ultrasound pullbacks

Tobias Wissel, Katharina Riedl, Klaus Schaefers, Hannes Nickisch, Fabian Brunner, Nikolas Schnellbaecher, Stefan Blankenberg, Moritz Seiffert, Michael Grass

Proceedings Volume 11598, 115980Y (2021) https://doi.org/10.1117/12.2580710

KEYWORDS: Intravascular ultrasound, Computer programming, Heart, Image segmentation, Cardiology, Calcium

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