Dr. Haissam Ragab Profile

Dr. Haissam Ragab

at Universitätsklinikum Hamburg-Eppendorf

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Publications (1)

Proceedings Article | 15 February 2021 Poster + Presentation + Paper

Machine-learning-based clinical plaque detection using a synthetic plaque lesion model for coronary CTA

Nikolas Schnellbächer, Haissam Ragab, Hannes Nickisch, Tobias Wissel, Clemens Spink, Gunnar Lund, Michael Grass

Proceedings Volume 11597, 115972T (2021) https://doi.org/10.1117/12.2582195

KEYWORDS: Solid modeling, Diagnostics, Nuclear medicine, Neural networks, Image-guided intervention, Image classification, Computer programming, Computer aided diagnosis and therapy, Computed tomography, Arteries

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Coronary computed tomography angiography (coronary CTA) is a robust and well-established non-invasive diagnostic tool to detect and assess coronary artery disease (CAD). The accurate detection, quantification and characterization of the coronary plaque burden has become an important part of this imaging modality. The quality and performance of modern machine-learning-based data-driven learning approaches is often impacted by either insufficient or inconsistently-labeled training data and is further subject to additional bias from human annotators. To address these shortcomings for coronary plaque characterization, we have developed a synthetic lesion generating framework for CTA applications, which can produce accurate and high-quality labeled training data for data-driven learning approaches. This approach can help to ease the manual annotation burden, which is often the limiting factor in data-driven learning algorithms and instead provides reliable ground truth data for modern deep learning approaches. Furthermore, this framework can easily be used to create custom tailored training data that can be used for pre- or post-training steps of already existing machine learning approaches for CTA applications. We tested this data generation framework by inserting synthetic lesions in 11 clinical CTA scans of healthy patients resulting in a data set of ~7000 annotated 2D slices. With this data we performed several plaque detection experiments using a data-driven machine learning approach with a neural encoder architecture. In this plaque classification task we first demonstrate that the synthetic lesion generation module can consistently perform well in recognizing unseen synthetic test data with an overall classification accuracy of 93%. Next we apply the synthetic lesion framework in a transfer learning experiment, where we demonstrate the feasibility to learn to classify real clinical plaque lesions with a purely synthetic model (overall classification accuracy 84%) that never saw real clinical lesions during model training. Second, we show that using synthetically data for pre-training with a subsequent training on clinical data can enhance the overall classification accuracy (from 91% to 92%) while strongly increasing the true positive count. We conclude that the synthetic plaque lesions model faithfully covers many important image characteristics of real plaque lesions in coronary CTA imaging and can thus help reduce the annotation burden for data-driven predictive vascular systems in this domain. This allows the creation of exhaustively annotated and site-specific customizable training data with a computationally fast forward model.

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