Dr. Michail Fanariotis Profile

Dr. Michail Fanariotis

at GE Healthcare

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Proceedings Article | 2 April 2024 Presentation + Paper

Deep learning-based head and neck segmentation in CT angiography

Prakhar Prakash, Theodore Colwell, Mara Noskowiak, Michail Fanariotis, Sandeep Dutta

Proceedings Volume 12930, 1293003 (2024) https://doi.org/10.1117/12.3006798

KEYWORDS: Bone, Education and training, Image segmentation, Data modeling, 3D modeling, Performance modeling, Head, Computed tomography, Anatomy, Deep learning

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The identification of pathologies in CT Angiography (CTA) is a laborious process. Even with the use of advanced post-processing techniques (such as Maximum Intensity Projection (MIP) and Volume Rendering (VR)) the analysis of the head and neck vasculature is a challenging task due to the interference from the surrounding osseous anatomy. To address these issues, we introduce an innovative solution in the form of an Artificial Intelligence (AI) reconstruction system. This system is supported by a 3D convolutional neural network, specially trained to automate the process of CTA reconstruction in healthcare services. In this study, we demonstrate a novel solution based on Deep Learning (DL) for the purpose of automatically segmenting skeletal structures, calcified plaque, and arterial vessels within CTA images. The advanced DL segmentation models that have been developed can perform accurately across different anatomies, scans, and reconstruction settings and allow superior visualization of vascular anatomy and pathology compared to other conventional techniques. These models have shown remarkable performance with a mean dice score of 0.985 for the bone structures. This high score, attained on an independent validation dataset that was kept separate during the training process, reflects the model's strength and potential for reliable application in real-world settings.

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