Abstract
Computer assisted surgical planning and image guided technology have become increasingly used in neurosurgery. We have developed a system based on ATmC (Adaptive Template moderated Classification) for the automated segmentation of 3D MRI brain data sets of patients with brain tumors (meningiomas and low grade gliomas) into the skin, the brain, the ventricles and the tumor. In a validation study of 13 patients with brain tumors, the segmentation results of the automated method are compared to manual segmentations carried out by 4 independent trained human observers. It is shown that the automated method segments brain and tumor with accuracy comparable to the manual method and with improved reproducibility.
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Black, P.M., Moriarty, T., Alexander, E., Stieg, P., Woodward, E.J.: Development and implementation of intraoperative magnetic resonance imaging and its neurosurgical applications. Neurosurgery 41, 831–845 (1997)
Clark, M.: Knowledge Guided Processing of Magnetic Resonance Images of the Brain. PhD thesis, University of South Florida, Tampa, Florida (1998)
Collins, D.L., Peters, T.M., Dai, W., Evans, A.C.: Model based segmentation of individual brain structures from MRI data. SPIE Visualization in Biomedical Computing 1808, 10–23 (1992)
Dengler, J., Schmidt, M.: The dynamic pyramid - a model for motion analysis with controlled continuity. International Journal of Pattern Analysis and Machine Intelligence 2(2), 275–286 (1987)
Duda, R.O., Hart, P.E.: Pattern Classification and Scene Analysis. John Wiley and Sons, New York (1973)
Gerig, G., Kikinis, R., Kübler, O., Jolesz, F.A.: Nonlinear anisotropic filtering of MRI data. IEEE Transactions on Medical Imaging 11(2), 221–232 (1992)
Gerig, G., Martin, J., Kikinis, R., Kübler, O., Shenton, M., Jolesz, F.A.: Unsupervised tissue type segmentation of 3D dual-echo MR head data. Image and Vision Computing 10(6), 349–360 (1992)
Gibbs, P., Buckley, D.L., Blackband, S.J., Horsman, A.: Tumor volume determination from MR images by morphological segmentation. Physics in Medicine and Biology 41, 2437–2446 (1996)
Jolesz, F.: Image-guided procedures and the operating room of the future. Radiology 204, 601–612 (1997)
Just, M., Thelen, M.: Tissue characterization with T1, T2, and proton density values: Results in 160 patients with brain tumors. Radiology 169, 779–785 (1988)
Kamber, M., Shinghal, R., Collins, D.L., Francis, G.S., Evans, A.C.: Model-based 3- D segmentation of multiple sclerosis lesions in magnetic resonance brain images. IEEE Transactions on Medical Imaging 14(3), 442–453 (1995)
Kikinis, R., Gleason, P.L., Moriarty, T.M., Moore, M.R., Alexander III, E., Stieg, P.E., Matsumae, M., Lorensen, W.E., Cline, H.E., Black, P.M., Jolesz, F.A.: Computer assisted interactive three-dimensional planning for neurosurgical procedures. Neurosurgery 38(4), 640–651 (1996)
Kikinis, R., Warfield, S.K., Westin, C.F.: High performance computing (HPC) in medical image analysis (MIA) at the surgical planning laboratory (SPL). In: Proceedings of the 3rd High Performance Computing Asia Conference & Exhibition (1998)
Nakajima, S., Atsumi, H., Bhalerao, A., Jolesz, F.A., Kikinis, R., Yoshimine, T., Moriarty, T., Stieg, P.: Computer-assisted surgical planning for cerebrovascular neurosurgery. Neurosurgery 41, 403–409 (1997)
Roberts, D.W., Strohbehn, J.W., Hatch, J.F., Murray, W., Kettenberger, H.: A frameless stereotaxic integration of computerized tomographic imaging and the operating microscope. Journal of Neurosurgery 65, 545–549 (1986)
Serra, J.: Image analysis and mathematical morphology. London Academic, London (1982)
Velthuizen, R.P., Clarke, L.P., Phuphanich, S., Hall, L.O., Bensaid, A.M., Arrington, J.A., Greenberg, H.M., Silbinger, M.L.: Unsupervised measurement of brain tumor volume on MR images. JMRI 5, 594–605 (1995)
Vinitski, S., Gonzalez, C., Mohamed, F., Iwanaga, T., Knobler, R.L., Khalili, K., Mack, J.: Improved intracranial lesion characterization by tissue segmentation based on a 3D feature map. Magn Res Med 37, 457–469 (1997)
Warfield, S.K., Kaus, M., Jolesz, F., Kikinis, R.: Adaptive template moderated spatially varying statistical classification. In: Proceedings of the 1st International Conference on Medical Image Computing and Computer-Assisted Intervention, pp. 431–438. Boston, MA (1998)
Warfield, S.K., Dengler, J., Zaers, J., Guttmann, C.R.G., Wells, W.M., Ettinger, G.J., Hiller, J., Kikinis, R.: Hiller, and R Kikinis. Automatic identification of grey matter structures from MRI to improve the segmentation of white matter lesions. Journal of Image Guided Surgery 1(6), 326–338 (1995)
Watanabe, E., Watanabe, T., Manaka, S., Mayanagi, Y., Takakura, K.: Threedimensional digitizer (neuronavigator): new equipment for computed tomographyguided stereotaxic surgery. Surgical Neurology 27(6), 543–547 (1987)
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Kaus, M.R. et al. (1999). Segmentation of Meningiomas and Low Grade Gliomas in MRI. In: Taylor, C., Colchester, A. (eds) Medical Image Computing and Computer-Assisted Intervention – MICCAI’99. MICCAI 1999. Lecture Notes in Computer Science, vol 1679. Springer, Berlin, Heidelberg. https://doi.org/10.1007/10704282_1
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DOI: https://doi.org/10.1007/10704282_1
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-66503-8
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