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Abstract #1375

Improved Bayesian Brain MR Image Segmentation by Incorporating Subspace-Based Spatial Prior into Deep Neural Networks

Yunpeng Zhang1, Huixiang Zhuang1, Ziyu Meng1, Ruihao Liu1,2, Wen Jin2,3, Wenli Li1, Zhi-Pei Liang2,3, and Yao Li1
1School 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

Synopsis

Keywords: Machine Learning/Artificial Intelligence, SegmentationAccurate segmentation of brain tissues is important for brain imaging applications. Learning the high-dimensional spatial-intensity distributions of brain tissues is challenging for classical Bayesian classification and deep learning-based methods. This paper presents a new method that synergistically integrate a tissue spatial prior in the form of a mixture-of-eigenmodes with deep learning-based classification. Leveraging the spatial prior, a Bayesian classifier and a cluster of patch-based position-dependent neural networks were built to capture global and local spatial-intensity distributions, respectively. By combining the spatial prior, Bayesian classifier, and the proposed networks, our method significantly improved the segmentation performance compared with the state-of-the-art methods.

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