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Abstract #3489

Improved Segmentation of MR Brain Images by Integrating Bayesian and Deep Learning-Based Classification

Ruihao Liu1, Ziyu Meng1, Wenli Li1, Yao Li1, Yiping P. Du1, and Zhi-Pei Liang2,3
1Institute for Medical Imaging Technology, School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai, China, 2Beckman Institute for Advanced Science and Technology, University of Illinois at Urbana-Champaign, Urbana, IL, United States, 3Department of Electrical and Computer Engineering, University of Illinois at Urbana-Champaign, Urbana, IL, United States

Accurate segmentation of brain tissues is essential for brain imaging applications. Classical Bayesian methods rely on good probability functions to produce good segmentation results. This paper presents a new method to synergistically integrate classical Bayesian segmentation with deep learning-based classification. A cluster of patch-based position-dependent neural networks were trained to effectively capture the joint spatial-intensity distributions of brain tissues. This cluster of patch networks significantly extends the capability of classical Markov Random field models and conventional statistical brain atlases. By combining the classical Bayesian classifier with the proposed networks, our method significantly improved segmentation results compared with the state-of-art methods.

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