title: Brain Tumor Segmentation Using Deep Belief Networks and 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.
authors.affiliation: School of Computer Science & Communications Engineering, Jiangsu University, Zhenjiang, Jiangsu 212013, China.
authors.forename: Tianming
authors.initials: T
authors.lastname: Zhan
authors.forename: Yi
authors.initials: Y
authors.lastname: Chen
authors.forename: Xunning
authors.initials: X
authors.lastname: Hong
authors.affiliation: Jiangsu Key Laboratory of Meteorological Observation and Information Processing, Nanjing University of Information Science and Technology, Nanjing, Jiangsu, 210044, China.
authors.forename: Zhenyu
authors.initials: Z
authors.lastname: Lu
authors.forename: Yunjie
authors.initials: Y
authors.lastname: Chen
mesh_terms: D001921:Brain / Q000000981:diagnostic imaging* / Q000473:pathology; D001932:Brain Neoplasms / Q000000981:diagnostic imaging* / Q000473:pathology; D003198:Computer Simulation; D005910:Glioma / Q000000981:diagnostic imaging / Q000473:pathology; D006801:Humans; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D008279:Magnetic Resonance Imaging / Q000379:methods*; D008959:Models, Neurological; D010363:Pattern Recognition, Automated / Q000379:methods*
keywords: Brain tumor segmentation; deep belief networks; graph cut; pathological knowledge