A multi-label fusion based level set method for multiple sclerosis lesion segmentation_Journal of Biomedical Engineering

Authors：

GONG Zhaoxuan ^1,3 , GUO Wei ¹ , ZHANG Guoxu ² , GUO Jia ² , ZHU Zhenyu ¹ , TAN Wenjun ³ ,  ZHANG Guodong ¹

1. School of Computer Science, Shenyang Aerospace University, Shenyang 110136, P.R.China;
2. The General Hospital of Shenyang Military, Shenyang 110016, P.R.China;
3. Key Laboratory of Intelligent Computing in Medical Image, Ministry of Education, Shenyang 110819, P.R.China;

Corresponding author：

ZHANG Guodong, Email: 20180008@sau.edu.cn

Keywords：

magnetic resonance image; multiple sclerosis lesion; level set; intensity prior information; multi-label fusion

DOI：

10.7507/1001-5515.201808042

Video：

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Abstract Full text Figures/Tables Video References Cited by

A multi-label based level set model for multiple sclerosis lesion segmentation is proposed based on the shape, position and other information of lesions from magnetic resonance image. First, fuzzy c-means model is applied to extract the initial lesion region. Second, an intensity prior information term and a label fusion term are constructed using intensity information of the initial lesion region, the above two terms are integrated into a region-based level set model. The final lesion segmentation is achieved by evolving the level set contour. The experimental results show that the proposed method can accurately and robustly extract brain lesions from magnetic resonance images. The proposed method helps to reduce the work of radiologists significantly, which is useful in clinical application.

Citation： GONG Zhaoxuan, GUO Wei, ZHANG Guoxu, GUO Jia, ZHU Zhenyu, TAN Wenjun, ZHANG Guodong. A multi-label fusion based level set method for multiple sclerosis lesion segmentation. Journal of Biomedical Engineering, 2019, 36(3): 453-459. doi: 10.7507/1001-5515.201808042 Copy

1.	Jain S, Sima D M, Ribbens A, et al. Automatic segmentation and volumetry of multiple sclerosis brain lesions from MR images. Neuroimage Clin, 2015, 8: 367-375.
2.	Yoo B I, Lee J J, Han J W, et al. Application of variable threshold intensity to segmentation for white matter hyperintensities in fluid attenuated inversion recovery magnetic resonance images. Neuroradiology, 2014, 56(4): 265-281.
3.	Cabezas M, Oliver A, Roura E, et al. Automatic multiple sclerosis lesion detection in brain MRI by FLAIR thresholding. Comput Methods Prog Biomed, 2014, 115(3): 147-161.
4.	Ganiler O, Oliver A, Diez Y, et al. A subtraction pipeline for automatic detection of new appearing multiple sclerosis lesions in longitudinal studies. Neuroradiology, 2014, 56(5): 363-374.
5.	Guo Wei, Li Qiang. Effect of segmentation algorithms on the performance of computerized detection of lung nodules in CT. Med Phys, 2014, 41(9): 1-8.
6.	Garcia-Lorenzo D, Francis S, Narayanan S A, et al. Review of automatic segmentation methods of multiple sclerosis white matter lesions on conventional magnetic resonance imaging. Med Image Anal, 2013, 17(1): 1-18.
7.	Li, Chunming, Gore J C, Davatzikos C. Multiplicative intrinsic component optimization (MICO) for MRI bias field estimation and tissue segmentation. Magnetic Resonance Imaging, 2014, 32(7): 913-923.
8.	Li Chunming, Xu Chenyang, Gui Changfeng, et al. Distance regularized level set evolution and its application to image segmentation. IEEE Trans Image Process, 2010, 19(12): 3243-3254.
9.	Li Chunming, Huang Rui, Ding Zhaohua, et al. A variational level set approach to segmentation and bias correction of images with intensity inhomogeneity// 11th International Conference on Medical Image Computing and Computer-Assisted Intervention (MICCAI). New Youk: Springer Berlin Heidelberg, 2008, 11(Pt 2): 1083-1091.
10.	Zhao Yue, Guo Shuxu, Luo Min, et al. An energy minimization method for MS lesion segmentation from T1-w and FLAIR images. Magn Reson Imaging, 2017, 39: 1-6.
11.	Roy S, Bhattacharyya D, Bandyopadhyay S K, et al. An effective method for computerized prediction and segmentation of multiple sclerosis lesions in brain MRI. Comput Methods Programs Biomed, 2017, 140: 307-320.
12.	Valverde S, Cabezas M, Roura E, et al. Improving automated multiple sclerosis lesion segmentation with a cascaded 3D convolutional neural network approach. NeuroImage, 2017, 155: 159-168.
13.	Guizard N, Coupé P, Fonov V S, et al. Rotation-invariant multi-contrast non-local means for MS lesion segmentation. NeuroImage: Clinical, 2015, 8: 376-389.
14.	Filho A C. A hybrid approach based on logistic classification and iterative contrast enhancement algorithm for hyperintense multiple sclerosis lesion segmentation. Med Biol Eng Comput, 2018, 56: 1063-1076.
15.	Doshi J, Erus G, Ou Yangming, et al. Multi-atlas skull stripping. Academic Radiology, 2013, 20(12): 1566-1576.
16.	Cai Weiling, Chen Songcan, Zhang Daoqiang. Fast and robust fuzzy c-means clustering algorithms incorporating local information for image segmentation. Pattern Recognition, 2007, 40(3): 825-838.
17.	Gong Zhaoxuan, Lu Zhentai, Zhao Dazhe, et al. Level set framework of multi-atlas label fusion with applications to magnetic resonance imaging segmentation of brain region of interests and cardiac left ventricles. Digit Med, 2017, 3(2): 76-85.
18.	Zhao Yue, Guo Shuxu, Luo Min, et al. A level set method for multiple sclerosis lesion segmentation. Magn Reson Imaging, 2018, 49: 94-100.
19.	Gong Zhaoxuan, Zhao Dazhe, Li Chunming, et al. A robust energy minimization algorithm for MS-lesion segmentation// 11th International Symposium on Visual Computing (ISVC), Las Vegas, 2015, 9474: 521-530.

1. Jain S, Sima D M, Ribbens A, et al. Automatic segmentation and volumetry of multiple sclerosis brain lesions from MR images. Neuroimage Clin, 2015, 8: 367-375.
2. Yoo B I, Lee J J, Han J W, et al. Application of variable threshold intensity to segmentation for white matter hyperintensities in fluid attenuated inversion recovery magnetic resonance images. Neuroradiology, 2014, 56(4): 265-281.
3. Cabezas M, Oliver A, Roura E, et al. Automatic multiple sclerosis lesion detection in brain MRI by FLAIR thresholding. Comput Methods Prog Biomed, 2014, 115(3): 147-161.
4. Ganiler O, Oliver A, Diez Y, et al. A subtraction pipeline for automatic detection of new appearing multiple sclerosis lesions in longitudinal studies. Neuroradiology, 2014, 56(5): 363-374.
5. Guo Wei, Li Qiang. Effect of segmentation algorithms on the performance of computerized detection of lung nodules in CT. Med Phys, 2014, 41(9): 1-8.
6. Garcia-Lorenzo D, Francis S, Narayanan S A, et al. Review of automatic segmentation methods of multiple sclerosis white matter lesions on conventional magnetic resonance imaging. Med Image Anal, 2013, 17(1): 1-18.
7. Li, Chunming, Gore J C, Davatzikos C. Multiplicative intrinsic component optimization (MICO) for MRI bias field estimation and tissue segmentation. Magnetic Resonance Imaging, 2014, 32(7): 913-923.
8. Li Chunming, Xu Chenyang, Gui Changfeng, et al. Distance regularized level set evolution and its application to image segmentation. IEEE Trans Image Process, 2010, 19(12): 3243-3254.
9. Li Chunming, Huang Rui, Ding Zhaohua, et al. A variational level set approach to segmentation and bias correction of images with intensity inhomogeneity// 11th International Conference on Medical Image Computing and Computer-Assisted Intervention (MICCAI). New Youk: Springer Berlin Heidelberg, 2008, 11(Pt 2): 1083-1091.
10. Zhao Yue, Guo Shuxu, Luo Min, et al. An energy minimization method for MS lesion segmentation from T1-w and FLAIR images. Magn Reson Imaging, 2017, 39: 1-6.
11. Roy S, Bhattacharyya D, Bandyopadhyay S K, et al. An effective method for computerized prediction and segmentation of multiple sclerosis lesions in brain MRI. Comput Methods Programs Biomed, 2017, 140: 307-320.
12. Valverde S, Cabezas M, Roura E, et al. Improving automated multiple sclerosis lesion segmentation with a cascaded 3D convolutional neural network approach. NeuroImage, 2017, 155: 159-168.
13. Guizard N, Coupé P, Fonov V S, et al. Rotation-invariant multi-contrast non-local means for MS lesion segmentation. NeuroImage: Clinical, 2015, 8: 376-389.
14. Filho A C. A hybrid approach based on logistic classification and iterative contrast enhancement algorithm for hyperintense multiple sclerosis lesion segmentation. Med Biol Eng Comput, 2018, 56: 1063-1076.
15. Doshi J, Erus G, Ou Yangming, et al. Multi-atlas skull stripping. Academic Radiology, 2013, 20(12): 1566-1576.
16. Cai Weiling, Chen Songcan, Zhang Daoqiang. Fast and robust fuzzy c-means clustering algorithms incorporating local information for image segmentation. Pattern Recognition, 2007, 40(3): 825-838.
17. Gong Zhaoxuan, Lu Zhentai, Zhao Dazhe, et al. Level set framework of multi-atlas label fusion with applications to magnetic resonance imaging segmentation of brain region of interests and cardiac left ventricles. Digit Med, 2017, 3(2): 76-85.
18. Zhao Yue, Guo Shuxu, Luo Min, et al. A level set method for multiple sclerosis lesion segmentation. Magn Reson Imaging, 2018, 49: 94-100.
19. Gong Zhaoxuan, Zhao Dazhe, Li Chunming, et al. A robust energy minimization algorithm for MS-lesion segmentation// 11th International Symposium on Visual Computing (ISVC), Las Vegas, 2015, 9474: 521-530.

Journal of Biomedical Engineering

A multi-label fusion based level set method for multiple sclerosis lesion segmentation

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