Dr. Kareem Tawfik Profile

Dr. Kareem Tawfik

at Vanderbilt Univ Medical Ctr

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Proceedings Article | 4 April 2022 Presentation + Paper

Acoustic neuroma segmentation using ensembled convolutional neural networks

Qibang Zhu, Hao Li, Nathan Cass, Nathan Lindquist, Kareem Tawfik, Ipek Oguz

Proceedings Volume 12036, 120360T (2022) https://doi.org/10.1117/12.2613402

KEYWORDS: 3D modeling, Magnetic resonance imaging, Data modeling, Image segmentation, Performance modeling, Tumors, Acoustics, Convolutional neural networks, 3D magnetic resonance imaging, 3D acquisition

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Acoustic neuroma (AN) is a noncancerous and slow-growing tumor that influences the human hearing system. Magnetic resonance images (MRIs) are routinely utilized to monitor tumor progression. Quantifying tumor growth in an automated manner would allow more precise studies, both at the population level and for the clini- cal management of individual patients. In recent years, deep learning methods have shown excellent performance for many medical image segmentation tasks. However, most current methods do not work well on heterogeneous datasets where MRIs are acquired with vastly different protocols. In this paper, we propose a deep learning framework with ensembled convolutional neural networks (CNNs) to segment acoustic neuromas even in hetero- geneous datasets. We ensemble a 2.5D CNN model and a 3D CNN model together, with augmentations added to the model for better inter-dataset segmentation performance. We test our methods on two datasets: the publicly available dataset from the crossMoDA challenge and an in-house dataset. We examine our method with supervised learning on the crossMoDA dataset and directly apply the trained model to the in-house dataset. We use the Dice score, average surface distance (ASD), and 95-percent Hausdorff distance (95HD) as evaluation metrics. Our method has better performance than the baseline methods, not only on intra-dataset segmentation accuracy but also on inter-dataset generalizability.

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