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| Data-Driven Automatic Segmentation Algorithm for Trigeminal Nerve Fiber |
| Jin Er1, Feng Yuanjing1,2*, Zeng Qingrun1, Chen Yukai1, Huang Shengwei3,4, Ruan Linhui3,4 |
1(Institute of Information Processing and Automation,School of Information Engineering,Zhejiang University of Technology,Hangzhou 310023,China)
2(Zhejiang Provincial United Key Laboratory of Embedded Systems,Hangzhou 310023,China)
3(Department of Neurosurgery,The First Affiliated Hospital of Wenzhou Medical University,Wenzhou 325000,Zhejiang,China)
4(Zhejiang Provincial Key Laboratory of Aging and Neurological Disorder Research,Wenzhou 325000,Zhejiang,China) |
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Abstract A common problem in the process of trigeminal nerve fiber tractography is artificial dependence,mainly including artificial rendering of region of interest (ROI) and manual screening of target fibers,which generally results in uncertainty and data errors. To ovecome this problem,a data-driven automatic trigeminal nerve fiber segmentation algorithm was proposed in this paper. A data-driven fiber clustering atlas was established based on the fiber data of several groups of brain samples,which automatically segmented the fiber data of new samples and directly obtained the trigeminal nerve fibers. In experiments,25 groups of healthy young individuals were selected as samples. Firstly,the brainstem was extracted by FSL software segmentation tool as ROI for deterministic fiber tracking. Secondly,a data-driven clustering atlas of fibers was created by multi-sample registration and spectral clustering of 20 groups of fibers. According to the tiny characteristics of trigeminal nerve,the trigeminal nerve fibers were labeled by secondary classification of brainstem fibers in the process of establishing fiber atlas. Finally,new sample data of 5 groups of healthy young people were selected,and their brainstem fiber data were automatically segmented using fiber atlas to obtain trigeminal nerve fiber bundles,and theweighted Dice coefficient between the results of automatic segmentation and manual segmentation of the same sample data was calculated. Results showed that the proposed method successfully segmented 5 sets of trigeminal nerve fiber bundles while the conventional manual method successfully identified 4 sets. The weighted Dice coefficients between the two results were 0.865,0.939,0.824,and 0.942. These results showed that this method can effectively avoid the influence of human factors,and greatly improve the work efficiency of neurosurgeons and cranial nerve researchers.
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Received: 19 August 2019
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[1] Hughes MA,Frederickson AM,Branstetter BF,et al.MRI of the trigeminal nerve in patients with trigeminal neuralgia secondary to vascular compression[J].American Journal of Roentgenology,2016,206(3):595-600.
[2] 颜剑豪,全显跃,江桂华.三叉神经痛的磁共振扩散张量成像研究[J].中华神经医学杂志,2010,9(5):508-511.
[3] Borges A,Casselman J.Imaging the trigeminal nerve[J].European Journal of Radiology,2010,74(2):323-340.
[4] Basser PJ,Mattiello J,LeBihan D.MR diffusion tensor spectroscopy and imaging[J].Biophysical Journal,1994,66(1):259-267.
[5] Basser P J,Mattiello J,Lebihan D.Estimation of the effective self-diffusion tensor from the NMR spin echo[J].Journal of Magnetic Resonance,Series B,1994,103(3):247-254.
[6] Kabasawa H,Masutani Y,Aoki S,et al.3T PROPELLER diffusion tensor fiber tractography:A feasibility study for cranial nerve fiber tracking[J].Radiat Med,2007,25(9):462-466.
[7] Tuch DS.Q-ball imaging[J].Magnetic Resonance in Medicine,2004,52(6):1358-1372.
[8] Tournier JD,Calamante F,Gadian GD,et al.Direct estimation of the fiber orientation density function from diffusion-weighted MRI data using spherical deconvolution[J].Neuroimage,2004,23(3):1176-1185.
[9] Jeurissen B,Tournier JD,Dhollander T,et al.Multi-tissue constrained spherical deconvolution for improved analysis of multi-shell diffusion MRI data[J].NeuroImage,2014,103:411-426.
[10] Wedeen VJ,Hagmann P,Tseng IW,et al.Mapping complex tissue architecture with diffusion spectrum magnetic resonance imaging[J].Magnetic Resonance in Medicine,2010,54(6):1377-1386.
[11] 冯远静,何建忠,李永强,等.神经纤维体素微结构成像估计算法研究进展[J].中国科学信息科学,2019,49(6):663-684.
[12] Yoshino M,Abhinav K,Yeh FC,et al.Visualization of cranial nerves using high-definition fiber tractography[J].Neurosurgery,2016,79(1):146-165.
[13] 郭油油,李咏梅,刘义,等.纤维示踪成像技术对多发性硬化引起的三叉神经痛病人的影像学研究[J].中国疼痛医学杂志,2017,23(10):743-747.
[14] Jacquesson T,Carole F,Gabriel K,et al.Overcoming challenges of cranial nerve tractography:A targeted review[J].Neurosurgery,2019,84(2):313-325.
[15] O′Donnell LJ,Westin CF.Automatic tractography segmentation using a high-dimensional white matter atlas[J].IEEE Transactions on Medical Imaging,2007,26(11):1562-1575.
[16] Zhang Fan,Wu Ye,Norton I,et al.An anatomically curated fiber clustering white matter atlas for consistent white matter tract parcellation across the lifespan[J].NeuroImage,2018,179(1):429-447.
[17] Zhang Fan,Wu Weining,Ning Lipeng,et al.Suprathreshold fiber cluster statistics:Leveraging white matter geometry to enhance tractography statistical analysis[J].NeuroImage,2018,171(1):314-354.
[18] O′Donnell LJ,Suter Y,Rigolo L,et al.Automated white matter fiber tract identification in patients with brain tumors[J].NeuroImage:Clinical,2017,13:138-153.
[19] Wu Ye,Zhang Fan,Makris N,et al.Investigation into local white matter abnormality in emotional processing and sensorimotor areas using an automatically annotated fiber clustering in major depressive disorder.[J].Neuroimage,2018,181:16-29
[20] O′Donnell JL,Wells WM,Golby AJ,et al.Unbiased groupwise registration of white matter tractography[C]//International Conference on Medical Image Computing and Computer-Assisted Intervention.Berlin:Springer,2012:123-130.
[21] Fowlkes C,Belongie S,Chung F,et al.Spectral grouping using the Nystrom method[J].IEEE transactions on pattern analysis and machine intelligence,2004,26(2):214-225.
[22] Dice LR.Measures of the amount of ecologic association between species[J].Ecology,1945,26(3):297-302.
[23] Cousineau M,Jodoin PM,Garyfallidis E,et al.A test-retest study on Parkinson′s PPMI dataset yields statistically significant white matter fascicles[J].Neuroimage Clinical,2017,16:222-233.
[24] Siless V,Chang K,Fischl B,et al.Hierarchical clustering of tractography streamlines based on anatomical similarity[J].NeuroImage,2018,166:32-45.
[25] Ge B,Guo L,Zhang T,et al.Group-wise consistent fiber clustering based on multimodal connectional and functional profiles[J].2012,15:485-492.
[26] Wang Q,Yap PT,Wu G,et al.Application of neuroanatomical features to tractography clustering[J].Human Brain Mapping,2013,34(9):2089-2101. |
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