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A study on spinal cord segmentation techniques

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Treść / Zawartość
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Warianty tytułu
Języki publikacji
EN
Abstrakty
EN
One of the vital organs, which manage the communication between the brain and different body parts, is the spinal cord. It is highly prone to the traumatic injuries and to several diseases. The vital criteria for the clinical management are the appropriate localization and segmentation of the spinal cord. The segmentation experiences the risks, associated with the diversity in the human anatomy and contrast variation inMagnetic Resonance Imaging (MRI). Hence, an efficacious segmentation method must be devised for the effective segmentation and disc localization of the spinal cord. Correspondingly, the here contained survey provides the review of the distinct segmentation schemes for the spinal cord segmentation. At present, there is an urgent requirement for the development of an effective segmentation approach so as to outperform the existing segmentation methods. In this research article, a detailed survey on several research works presenting the recommended segmentation schemes, based on the active contour model, semi-automated segmentation, deformable model, probabilistic model, graph-based segmentation, and so on, is presented. Additionally, an in depth analysis and discussion are provided, in accordance with the publication year, evaluation metrics, segmentation scheme, Magnetic Resonance (MR) image datasets, Dice Similarity Coefficient (DSC) and accuracy. Subsequently, the research gaps and risks, related to distinct segmentation schemes are considered for directing the researchers towards a better future investigation field.
Rocznik
Strony
497--521
Opis fizyczny
Bibliogr. 50 poz., rys.
Twórcy
autor
  • Noorul Islam University, Kumaracoil – 629 180, India
autor
  • Department of Information Technology, Noorul Islam University, Kumaracoil – 629 180, India
Bibliografia
  • [1] An, H.S., Anderson, P.A., Haughton, V.M., Iatridis, J.C., Kang, James D., Lotz, Jeffrey C., Natarajan, R. N. et al. (2004) Introduction: disc degeneration: summary. Spine, 29, 23, 2677-2678.
  • [2] Asman, A.J., Bryan, F.W., Smith, S.A., Reich, D.S. and Landman, B.A. (2014) Group wise multi-atlas segmentation of the spinal cord’s internal structure. Medical Image Analysis, 18, 3, 460-471.
  • [3] Cadotte, A., Cadotte, D. W., Livne, M., Cohen-Adad, J., Fleet, D., Mikulis, D. and Fehlings, M.G. (2015) Spinal cord segmentation by one dimensional normalized template matching: a novel, quantitative technique to analyze advanced magnetic resonance imaging data. PloS One, 10, 10, 1-18.
  • [4] Chen, Ch., Belavy, D., Yu, W., Chu, Ch., Armbrecht, G., Bansmann, M., Felsenberg, D. and Zheng, G. (2015) Localization and segmentation of 3D intervertebral discs in MR images by data driven estimation. IEEE Transactions on Medical Imaging, 34, 8, 1719-1729.
  • [5] Chen, M., Carass, A., Cuzzocreo, J., Bazin, P.-L., Reich, D.S. and Prince, J.L. (2011) Topology preserving automatic segmentation of the spinal cord in magnetic resonance images. In: Proceedings of 2011 IEEE International Symposium on Biomedical Imaging: From Nano to Macro. IEEE, 1737-1740.
  • [6] Chen, M., Carass, A., Oh, J., Nair, G., Pham, D.L., Reich, D.S. and Prince, J.L. (2013) Automatic magnetic resonance spinal cord segmentation with topology constraints for variable fields of view. NeuroImage, 83, 1051-1062.
  • [7] Chu, Ch., Belavy, D.L., Armbrecht, G., Bansmann, M., Felsenberg, D. and Zheng, G. (2015) Fully automatic localization and segmentation of 3d vertebral bodies from CT/MR images via a learning-based method. PloS One, 10, 11.
  • [8] Cohen-Adad, J., El Mendili, M.M., Lehericy, S., Pradat, P.-F., Blancho, S., Rossignol, S. and Benali, H. (2011) Demyelination and degeneration in the injured human spinal cord detected with diffusion and magnetization transfer MRI. NeuroImage, 55, 3, 1024-1033.
  • [9] Corso, J.J., Raja’S, A. and Chaudhary, V. (2008) Lumbar disc localization and labeling with a probabilistic model on both pixel and object features. In: Proceedings of International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2008, 202-210.
  • [10] Coulon, O., Hickman, S.J., Parker, G.J., Barker, G.J., Miller D. H. and Arridge, S.R. (2002) Quantification of spinal cord atrophy from magnetic resonance images via a B-spline active surface model. Magnetic Resonance in Medicine, 47, 6, 1176-1185.
  • [11] Datta, E., Papinutto, N., Schlaeger, R., Zhu, A., Carballido-Gamio, J. and Henry, R.G. (2017) Gray matter segmentation of the spinal cord with active contours in MR images. NeuroImage, 147, 788-799.
  • [12] De Leener, B., Cohen-Adad, J. and Kadoury, S. (2014) Automatic 3D segmentation of spinal cord MRI using propagated deformable models. In: Proceedings of International Society for Optics and Photonics, Medical Imaging 2014: Image Processing, S. Ourselin and M.A. Styner, eds. SPIE, 9034, 90343R.
  • [13] De Leener, B., Cohen-Adad, J. and Kadoury, S. (2015) Automatic segmentation of the spinal cord and spinal canal coupled with vertebral labeling. IEEE Transactions on Medical Imaging, 34, 8, 1705-1718.
  • [14] De Leener, B., Kadoury, S. and Cohen-Adad, J. (2014) Robust, accurate and fast automatic segmentation of the spinal cord. NeuroImage, 98, 528-536.
  • [15] De Leener, B., Taso, M., Cohen-Adad, J. and Callot, V. (2016) Segmentation of the human spinal cord. Magnetic Resonance Materials in Physics, Biology and Medicine, 29, 2, 125–153.
  • [16] Dupont, S.M., De Leener, B., Taso, M., Le Troter, A., Stikov, N., Callot, V. and Cohen-Adad, J. (2017) Fully integrated framework for the segmentation and registration of the spinal cord white and gray matter. NeuroImage, 150, 358-372.
  • [17] El Mendili, M.-M., Chen, R., Tiret, B., Pelegrini-Issac, M., Cohen- Adad, J., Lehericy, S., Pradat, P.-F. and Benali, H. (2015a) Val- idation of a semi-automated spinal cord segmentation method. Journal of Magnetic Resonance Imaging, 41, 2, 454-459.
  • [18] El Mendili, M.-M., Chen, R., Tiret, B., Villard, N., Trunet, S., Pelegrini-Issac, M., Lehericy, S., Pradat, P.-F. and Benali, H. (2015b) Fast and accurate semi-automated segmentation method of spinal cord MR images at 3T applied to the construction of a cervical spinal cord template. PloS One, 10, 3, 1-21.
  • [19] Ghosh, S. and Chaudhary, V. (2014) Supervised methods for detection and segmentation of tissues in clinical lumbar MRI. Computerized Medical Imaging and Graphics, 38, 7, 639-649.
  • [20] Ghosh, S., Raja’S, A., Chaudhary, V. and Dhillon, G. (2011) Composite features for automatic diagnosis of intervertebral disc herniation from lumbar MRI. In: Proceedings of 2011 Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC, 5068-5071.
  • [21] Griffith, J. F., Wang, Y.-X., Antonio, G.E., Choi, K.C., Yu, A., Ahuja, A.T. and Leung, P.C. (2007) Modified Pfirrmann grading system for lumbar intervertebral disc degeneration. Spine, 32, 24, E708-E712.
  • [22] Gros, C., De Leener, B., Badji, A. et al. (2018) Automatic segmentation of the spinal cord and intramedullary multiple sclerosis lesions with convolutional neural networks. Computer Vision and Pattern Recognition, under review.
  • [23] Hille, G., Saalfeld, S., Serowy, S. and Tonnies, K. (2018) Vertebral body segmentation in wide range clinical routine spine MRI data. Computer Methods and Programs in Biomedicine, 155, 93-99.
  • [24] Horsfield, M.A., Sala, S., Neema, M., Absinta, M., Bakshi, A., Sormani, M.P., Rocca, M.A., Bakshi, R. and Filippi, M. (2010) Rapid semi-automatic segmentation of the spinal cord from magnetic resonance images: application in multiple sclerosis. NeuroImage, 50, 2, 446-455.
  • [25] Kawahara, J., McIntosh, C., Tam, R. and Hamarneh, G. (2013a) Globally optimal spinal cord segmentation using a minimal path in high dimensions. In: Proceedings of 2013 IEEE 10th International Symposium on Biomedical Imaging (ISBI). IEEE, 848-851.
  • [26] Kawahara, J., McIntosh, C., Tam, R. and Hamarneh, G. (2013b) Augmenting auto-context with global geometric features for spinal cord segmentation. In: Proceedings of 4th International Workshop on Machine Learning in Medical Imaging. Lecture Notes in Computer Science 8184. Springer Verlag, 211-218.
  • [27] Koh, J., Chaudhary, V. and Dhillon, G. (2012) Disc herniation diagnosis in MRI using a CAD framework and a two level classifier. International Journal of Computer Assisted Radiology and Surgery, 7, 6, 861-869.
  • [28] Koh, J., Kim, T., Chaudhary, V. and Dhillon, G. (2010) Automatic segmentation of the spinal cord and the duralsac in lumbar MR images using gradient vector flow field. In: Proceedings of 2010 Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC). IEEE, 3117-3120.
  • [29] Koh, J., Scott, P.D., Chaudhary, V. and Dhillon, G. (2011) An automatic segmentation method of the spinal canal from clinical MR images based on an attention model and an active contour model. In: Proceedings of 2011 IEEE International Symposium on Biomedical Imaging: From Nano to Macro. IEEE, 1467-1471.
  • [30] Law, M. W. K., Tay, K., Leung, A., Garvin, G. J. and Li, S. (2013) Intervertebral disc segmentation in MR images using aniso-tropic oriented flux. Medical Image Analysis, 17, 1, 43-6.
  • [31] Liao, C.-C., Ting, H.-W. and Xiao, F. (2017) Atlas-Free Cervical Spinal Cord Segmentation on Midsagittal T2 Weighted Magnetic Resonance Images. Journal of Healthcare Engineering, 2017, 1-12.
  • [32] McIntosh, C., Hamarneh, G., Toom, M. and Tam, R.C. (2011) Spinal cord segmentation for volume estimation in healthy and multiple sclerosis subjects using crawlers and minimal paths. In: Proceedings of 2011 First IEEE International Conference on Healthcare Informatics, Imaging and Systems Biology (HISB). IEEE, 25-31.
  • [33] Michopoulou, S.K., Costaridou, L., Panagiotopoulos, E., Speller, R., Panayiotakis, G. and Todd-Pokropek, A. (2009) Atlas-based segmentation of degenerated lumbar intervertebral discs from MR images of the spine. IEEE Transactions on Biomedical Engineering, 56, 9, 2225-2231.
  • [34] Mukherjee, D.P., Cheng, I., Ray, N., Mushahwar, V., Lebel, M. and Basu, A. (2010) Automatic segmentation of spinal cord MRI using symmetric boundary tracing. IEEE Transactions on Information Technology in Biomedicine, 14, 5, 1275-1278.
  • [35] Nasiri, F. and Zade H. S. (2013) Automatic segmentation of interverte-bral disk from MR images of the spine based on graph cut method. In: Proceedings of 2013 8th IEEE Iranian Conference on Machine Vision and Image Processing (MVIP). IEEE, 300-303.
  • [36] Niemelainen, R., Videman, T., Dhillon, S.S. and Battie, M.C. (2008) Quantitative measurement of intervertebral disc signal using MRI. Clinical Radiology, 63, 3, 252-255.
  • [37] Oktay, A.B., Albayrak, N.B. and Akgul, Y.S. (2014) Computer aided diagnosis of degenerative intervertebral disc diseases from lumbar MR images. Computerized Medical Imaging and Graphics, 38, 7, 613-619.
  • [38] Orphanoudakis, S.C., Kaldoudi, E. and Tsiknakis, M. (1996) Technological advances in teleradiology. European Journal of Radiology, 22, 3, 205-217.
  • [39] Perone, C.S., Calabrese, E. and Cohen-Adad, J. (2017) Spinal cord gray matter segmentation using deep dilated convolutions. Scientific Reports, 8, 1-13.
  • [40] Prados, F., Cardoso, M.J., Yiannakas, M.C., Hoy, L.R., Tebaldi, E., Kearney, H., Liechti, M.D. et al. (2016) Fully automated grey and white matter spinal cord segmentation. Scientific Reports, 6, 1.
  • [41] Priya, R. and Umaibanu, M. (2017) Automatic Spinal Cord Segmentation From Medical MR Images using Hybrid Algorithms. International Journal on Future Revolution in Computer Science & Communication Engineering, 3, 12, 226 – 230.
  • [42] Raja’S, A., Corso, J.J., Chaudhary, V. and Dhillon, G. (2010) Computer-aided diagnosis of lumbar disc pathology from clinical lower spine MRI. International Journal of Computer Assisted Radiology and Surgery, 5, 3, 287-293.
  • [43] Riesenburger, R.I., Safain, M.G., Ogbuji, R., Hayes, J. and Hwang, S.W. (2015) A novel classification system of lumbar disc degeneration. Journal of Clinical Neuroscience, 22, 2, 346-351.
  • [44] Ruiz-Espana, S., Arana, E. and Moratal, D. (2015) Semiautomatic computer-aided classification of degenerative lumbar spine disease in magnetic resonance imaging. Computers in Biology and Medicine, 62, 196-205.
  • [45] Sahane, R.S. and Shinde, J.V. (2016) A Survey on Segmentation of Spine MR Images Using Superpixels. International Journal for Research in Engineering Application & Management (IJREAM), 02, 09.
  • [46] Tang, Z. and Pauli, J. (2011) Fully automatic extraction of human spine curve from MR images using methods of efficient intervertebral disk extraction and vertebra registration. International Journal of Computer Assisted Radiology and Surgery, 6, 1, 21-33.
  • [47] Urrutia, J., Besa, P., Campos, M., Cikutovic, P., Cabezon, M., Molina, M. and Cruz, J.P. (2016) The Pfirrmann classification of lumbar intervertebral disc degeneration: an independent inter- and intra- observer agreement assessment. European Spine Journal, 25, 9, 2728-2733.
  • [48] Yu, L.-P., Qian, W.-W., Yin, G.-Y., Ren, Y.-X. and Hu, Z.-Y. (2012) MRI assessment of lumbar intervertebral disc degeneration with lumbar degenerative disease using the Pfirrmann grading systems. PLoS One, 7,12.
  • [49] Zhu, X., He, X., Wang, P., He, Q., Gao, D., Cheng, J. and Wu, B. (2016) A method of localization and segmentation of intervertebral discs in spine MRI based on Gabor filter bank. Biomedical Engineering Online, 15, 1, 32.
  • [50] Zikos, M., Kaldoudi, E. and Orphanoudakis, S.C. (1997) DIPE: A distributed environment for medical image processing. Studies in Health Technology and Informatics, 465–469.
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-1a0481fa-7884-4f14-87d5-16684cc05c50
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