Brain Tumor Segmentation Using Deep Belief Networks and Pathological Knowledge

Title:Brain Tumor Segmentation Using Deep Belief Networks and Pathological Knowledge

Volume: 16 Issue: 2

Author(s): Tianming Zhan, Yi Chen, Xunning Hong, Zhenyu Lu and Yunjie Chen

Affiliation:

Keywords: Brain tumor segmentation, deep belief networks, graph cut, pathological knowledge.

Abstract: In this paper, we propose an automatic brain tumor segmentation method based on Deep Belief Networks (DBNs) and pathological knowledge. The proposed method is targeted against gliomas (both low and high grade) obtained in multi-sequence magnetic resonance images (MRIs). Firstly, a novel deep architecture is proposed to combine the multi-sequences intensities feature extraction with classification to get the classification probabilities of each voxel. Then, graph cut based optimization is executed on the classification probabilities to strengthen the spatial relationships of voxels. At last, pathological knowledge of gliomas is applied to remove some false positives. Our method was validated in the Brain Tumor Segmentation Challenge 2012 and 2013 databases (BRATS 2012, 2013). The performance of segmentation results demonstrates our proposal providing a competitive solution with stateof- the-art methods.

Cite this article as:

Zhan Tianming, Chen Yi, Hong Xunning, Lu Zhenyu and Chen Yunjie, Brain Tumor Segmentation Using Deep Belief Networks and Pathological Knowledge, CNS & Neurological Disorders - Drug Targets 2017; 16 (2) . https://dx.doi.org/10.2174/1871527316666170113101559