Mr. Luka Škrlj Profile

Luka Škrlj

at Univ of Ljubljana

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Proceedings Article | 2 April 2024 Presentation + Paper

Deep-learning-based landmark localization in 3D CT images of the heart: method and dataset comparison

Luka Škrlj, Matija Jelenc, Tomaž Vrtovec

Proceedings Volume 12926, 129260F (2024) https://doi.org/10.1117/12.3006326

KEYWORDS: Computed tomography, Deep learning, Machine learning, 3D image processing, Anatomy, Heart, Image processing, Medical imaging, Cardiovascular disorders

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Aortic valve morphometry plays a crucial role in understanding and diagnosing cardiovascular diseases. Precise localization of landmarks in three-dimensional (3D) computed tomography (CT) images of the heart, particularly landmarks on the aortic valve, enables accurate assessment of valve structure and dimensions. Such information is vital for planning surgical interventions, evaluating valve function, and monitoring disease progression. Reliable landmark localization methods aid clinicians in making informed decisions, leading to improved patient outcomes and enhanced overall cardiovascular healthcare. In this study, we present a comprehensive comparison of two landmark localization methods, i.e. the spatial configuration network (SCN) and communicative multi-agent reinforcement learning (C-MARL), for detecting six distinctive landmarks on the aortic cusps from 160 3D CT images of healthy and pathological subjects. Both methods were individually trained on images from 80 healthy subjects, and their robustness to new, unseen pathological images was assessed by evaluating the trained models on 40 images from healthy and 40 images from pathological subjects. SCN exhibited superior performance in accurately localizing landmarks in healthy subjects (mean distance ± standard deviation against reference landmarks of 1.14±0.78 mm), showcasing its proficiency in normal anatomy scenarios. On the other hand, C-MARL demonstrated remarkable adaptability to the complexity of pathology, yielding better results for pathological subjects (2.66±3.99 mm). Both methods offer valuable insights for biomedical imaging applications.

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