Automated brain tumor segmentation using spatial accuracy-weighted hidden Markov Random Field |
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| Authors: | Jingxin Nie Zhong Xue Tianming Liu Geoffrey S. Young Kian Setayesh Lei Guo Stephen T.C. Wong |
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| Affiliation: | 1. School of Computer Science and Engineering, Nanjing University of Science and Technology, Nanjing, 210094, China;2. Shaanxi Key Lab of Speech and Image Information Processing (SAIIP), School of Computer Science, Northwestern Polytechnical University, Xi''an, 710072, China;3. Centre for Multidisciplinary Convergence Computing (CMCC), School of Computer Science, Northwestern Polytechnical University, Xi''an, 710072, China;4. School of Computer and Software, Nanjing University of Information Science and Technology, Nanjing, 210044, China;1. School of Mechano-Electronic Engineering, Xidian University, No. 2 South Taibai Road, Xi’an, Shaanxi 710071, China;2. Computer Science Department, Aberystwyth University, Ceredigion, SY23 3FL, UK;3. School of Automation, Northwestern Polytechnical University, 127 West Youyi Road, Beilin District, Xi''an Shaanxi, 710072, P.R. China;4. Deepwise AI Lab, NO. 8 Haidian Street, Haidian District, Beijing, 100080, China;1. CSIRO Preventative Health Flagship, CSIRO Computational Informatics, The Australian e-Health Research Centre, Herston, QLD, Australia;2. The Florey Institute of Neuroscience and Mental Health, Parkville, VIC, Australia;3. Department of Medicine and Neurology, Melbourne Brain Centre at the Royal Melbourne Hospital, University of Melbourne, Parkville, VIC, Australia;4. Department of Radiology, Melbourne Brain Centre at the Royal Melbourne Hospital, University of Melbourne, Parkville, VIC, Australia;5. Department of Occupational Therapy, La Trobe University, Bundoora, VIC, Australia |
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| Abstract: | A variety of algorithms have been proposed for brain tumor segmentation from multi-channel sequences, however, most of them require isotropic or pseudo-isotropic resolution of the MR images. Although co-registration and interpolation of low-resolution sequences, such as T2-weighted images, onto the space of the high-resolution image, such as T1-weighted image, can be performed prior to the segmentation, the results are usually limited by partial volume effects due to interpolation of low-resolution images. To improve the quality of tumor segmentation in clinical applications where low-resolution sequences are commonly used together with high-resolution images, we propose the algorithm based on Spatial accuracy-weighted Hidden Markov random field and Expectation maximization (SHE) approach for both automated tumor and enhanced-tumor segmentation. SHE incorporates the spatial interpolation accuracy of low-resolution images into the optimization procedure of the Hidden Markov Random Field (HMRF) to segment tumor using multi-channel MR images with different resolutions, e.g., high-resolution T1-weighted and low-resolution T2-weighted images. In experiments, we evaluated this algorithm using a set of simulated multi-channel brain MR images with known ground-truth tissue segmentation and also applied it to a dataset of MR images obtained during clinical trials of brain tumor chemotherapy. The results show that more accurate tumor segmentation results can be obtained by comparing with conventional multi-channel segmentation algorithms. |
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