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9 March 2020 A generalizable deep-learning approach to anatomical modeling of brain vasculature (Conference Presentation)
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Proceedings Volume 11226, Neural Imaging and Sensing 2020; 1122611 (2020) https://doi.org/10.1117/12.2543864
Event: SPIE BiOS, 2020, San Francisco, California, United States
Abstract
Quantitative 3D analysis of brain vasculature is a fundamental problem with important applications, for which vessel segmentation is a first step. Traditional segmentation methods based on parametric models have limited accuracy. More recent techniques based on machine learning have promising results but limited generalization capability. We present a deep-learning based segmentation method that overcomes limitations of existing systems and demonstrates the ability to generalize to various imaging setups, samples including both in-vivo/ex-vivo data, with state-of-the-art results. We achieve so by exploiting several novel methods in deep learning, such as semi-supervised learning. We believe that our work will be another step forward towards improved large-scale neurovascular analysis.
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Waleed Tahir, Jiabei Zhu, Sreekanth Kura, Xiaojun Cheng, Rafat Damseh, Frederic Lesage, Sava Sakadžic, David A. Boas, and Lei Tian "A generalizable deep-learning approach to anatomical modeling of brain vasculature (Conference Presentation)", Proc. SPIE 11226, Neural Imaging and Sensing 2020, 1122611 (9 March 2020); https://doi.org/10.1117/12.2543864
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