Automatic segmentation, internal classification, and follow-up of optic pathway gliomas in MRI

Medical Image Analysis

Volumn 16, Issue 1, 2012, Pages 177-188

  Weizman, L ^a   Ben Sira, L ^b   Joskowicz, L ^a   Constantini, S ^b   Precel, R ^b   Shofty, B ^b   Ben Bashat, D ^b  

^a HEBREW UNIVERSITY OF JERUSALEM   (Israel)

^b TEL AVIV UNIVERSITY   (Israel)

Author keywords

Brain tumor; Multi sequence MRI; Neurofibromatosis; Optic pathway glioma; Segmentation

Indexed keywords

AUTOMATIC LOCALIZATION; AUTOMATIC METHOD; AUTOMATIC SEGMENTATIONS; BRAIN TUMORS; DATA SETS; DISEASE PROGRESSION; GREY-LEVEL; INTENSITY INFORMATION; MANUAL SEGMENTATION; MR IMAGES; NEUROFIBROMATOSIS; RELIABLE PERFORMANCE; ROBUST METHODS; TISSUE MODELS;

IMAGE SEGMENTATION; MAGNETIC RESONANCE IMAGING; TUMORS;

CLASSIFICATION (OF INFORMATION);

ARTICLE; CHILD; CLINICAL ARTICLE; DISEASE COURSE; FOLLOW UP; HUMAN; HUMAN TISSUE; LEARNING; NUCLEAR MAGNETIC RESONANCE IMAGING; OPTIC NERVE GLIOMA; PRESCHOOL CHILD; PRIORITY JOURNAL; RADIOLOGICAL PARAMETERS; RADIOLOGIST; SCHOOL CHILD; TUMOR CLASSIFICATION;

ALGORITHMS; HUMANS; IMAGE ENHANCEMENT; IMAGE INTERPRETATION, COMPUTER-ASSISTED; MAGNETIC RESONANCE IMAGING; OPHTHALMOSCOPY; OPTIC NERVE; OPTIC NERVE GLIOMA; PATTERN RECOGNITION, AUTOMATED; REPRODUCIBILITY OF RESULTS; SENSITIVITY AND SPECIFICITY;

EID: 82355173314     PISSN: 13618415     EISSN: 13618423     Source Type: Journal    
DOI: 10.1016/j.media.2011.07.001     Document Type: Article

References (40)

1. Ashburner J., Friston K. Nonlinear spatial normalization using basis functions. Human Brain Mapping 1999, 7:254-266.
2. Binning M., Liu J., Kestle J., Brockmeyer D., Walker M. Optic pathway gliomas: a review. Neurosurgical Focus 2007, 23:1-8.
3. Breu H., Gil J., Kirkpatrick D., Werman M. Linear time euclidean distance transform algorithms. IEEE Transactions on Pattern Analysis and Machine Intelligence 1995, 17:529-533.
4. Collewet G., Strzelecki M., Mariette F. Influence of MRI acquisition protocols and image intensity normalization methods on texture classification. Magnetic Resonance Imaging 2004, 22:81-91.
5. Constantin, A., Bajcsy, B., Nelson, C., 2010. Unsupervised segmentation of brain tissue in multivariate MRI. In: Proceedings of IEEE International Symposium on Biomedical Imaging: From Nano to Macro (ISBI 2010), Rotterdam, The Netherlands, pp. 89-92.
6. Correa D., Shi W., Thaler H., Cheung A., DeAngelis L., Abrey L. Longitudinal cognitive follow-up in low grade gliomas. Journal of Neuro-Oncology 2008, 86:321-327.
7. Corso J., Sharon E., Dube S., El-Saden S., Sinha U., Yuille A. Efficient multilevel brain tumor segmentation with integrated bayesian model classification. IEEE Transactions on Medical Imaging 2008, 27:629-640.
8. Cox, I., Hingorani, S., 1995. Dynamic histogram warping of image pairs for constant image brightness. In: Proceedings of International Conference on Image Processing (ICIP 1995), Washington, DC,USA, pp. 366-369.
9. Davies D., Bouldin D. A cluster separation measure. IEEE Transactions on Pattern Analysis and Machine Intelligence 1979, 1:224-227.
10. Emblem K., Nedregaard B., Hald J.K., Nome T., Due-Tonnessen P., Bjornerud A. Automatic glioma characterization from dynamic susceptibility contrast imaging: brain tumor segmentation using knowledge-based fuzzy clustering. Journal of Magnetic Resonance Imaging 2009, 30:1-10.
11. Fiorino C., Reni M., Bolognesi M., Cattaneo G., Calandrino R. Intra and interobserver variability in contouring prostate and seminal vesicles: implications for conformal treatment planning. Radiotherapy and Oncology 1998, 47:285-292.
12. Friston, K., Holmes, A., Ashburner, J., 1999. Statistical parametric mapping (SPM). http://www.fil.ion.ucl.ac.uk/spm/.
13. Gerig, G., Jomier, M., Chakos, M., 2001. Valmet: a new tool for assessing and improving 3D object segmentation. In: Proceedings of International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI 2001), vol. LNCS 2208, Utrecht, The Netherlands, pp. 516-523.
14. Giedd J., Snell J., Lange N., Rajpakse J., Casey B., Kozuch P., Vaituzis A., Vauss Y., Hamburger S., Kaysen D., Rapoport J. Quantitative magnetic resonance imaging of human brain development: ages 4-18. Cerebral Cortex 1996, 6:551-560.
15. Guillamo J., Creange A., Kalifa C., Grill J., Rodriguez D., Doz F., Barbarot S., Zerah M., Sanson M., Bastuji-Garin S., Wolkenstein P. Prognostic factors of CNS tumours in neurofibromatosis 1: a retrospective study of 104 patients. Brain 2002, 126:152-160.
16. Ho, S., Bullitt, E., Gerig, G., 2002. Level set evolution with region competition: automatic 3-D segmentation of brain tumors. In: Proceedings of International Conference on Pattern Recognition (ICPR 2002), Quebec, Canada, pp. 532-535.
17. Huson S., Hughes R. The Neurofibromatoses: A Pathogenetic and Clinical Overview 1994, Chapman & Hall, London.
18. Johns Hopkins Medical Institute, 2004. Laboratory of brain anatomical MRI. http://cmrm.med.jhmi.edu/.
19. Karayiannis N., Pai P. Segmentation of magnetic resonance images using fuzzy algorithms for learning vector quantization. IEEE Transactions on Medical Imaging 1999, 18:172-180.
20. Kaus M., Warfield S., Nabavi A., Black P., Jolesz F., Kikinis R. Automated segmentation of MRI of brain tumors. Radiology 2001, 218:586-591.
21. Kay S. Fundamentals of statistical signal processing: detection theory 1998, Prentice Hall, Englewood, NJ.
22. Konukoglu E., Clatz O., Menze B., Stieltjes B., Weber M., Mandonnet E., Delingette H., Ayache N. Image guided personalization of reaction-diffusion type tumor growth models using modified anisotropic eikonal equations. IEEE Transactions on Medical Imaging 2010, 29:77-95.
23. Kornreich L., Blaser S., Schwarz M., Shuper A., Vishne T., Cohen I., Faingold R., Michovitz S., Koplewitz B., Horev G. Optic pathway glioma: correlation of imaging findings with the presence of neurofibromatosis. Journal of Medical Genetics 2001, 22:1963-1969.
24. Lee, C., Schmidt, M., Murtha, A., Bistritz, A., Sander, J., Greiner, R., 2005. Segmenting brain tumor with conditional random fields and support vector machines. In: Proceedings of International Conference on Computer Vision (ICCV 2005), Beijing, China, pp. 469-478.
25. Liu J., Udupa J., Odhner D., Hackney D., Moonis G. A system for brain tumor volume estimation via MR imaging and fuzzy connectedness. Computerized Medical Imaging and Graphics 2005, 29:21-34.
26. Meier D., Guttmann C. Time-series analysis of MRI intensity patterns in multiple sclerosis. NeuroImage 2003, 10:1193-1209.
27. Menze, B., Leemput, K., Lashkari, D., Weber, M., Ayache, N., Golland, P., 2010. A generative model for brain tumor segmentation in multi-modal images. In: Proceedings of International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI 2010), vol. LNCS 6362, pp. 151-159.
28. Morita A., Kelly P. Resection of intraventricular tumors via a computer-assisted volumetric stereotactic approach. Neurosurgery 1993, 32:920-927.
29. Nie J., Xue Z., Liu T., Young G., Setayesh K., Guo L., Wong S. Automated brain tumor segmentation using spatial accuracy-weighted hidden markov random field. Computerized Medical Imaging and Graphics 2009, 33:431-441.
30. Pakzad-Vaezi K., Cochrane D., Sargent M., Singhal A. Conventional and diffusion-weighted magnetic resonance imaging findings in a pediatric patient with a posterior fossa brain tumor and papilledema. Pediatric Neurosurgery 2009, 45:414-418.
31. Park, B., Windham, W., Lawrence, K., Smith, D., 2003. Classification of hyperspectral imagery for identifying fecal and ingesta contaminants. In: Proceedings of SPIE, vol. 5721, pp. 118-127.
32. Prastawa M., Bullitt E., Bullitt N., Leemput K., Gerig G. Automatic brain tumor segmentation by subject specific modification of atlas priors. Academic Radiology 2003, 10:1341-1348.
33. Prastawa M., Ho S., Gerig G. A brain tumor segmentation framework based on outlier detection. Medical Image Analysis Journal 2004, 8:275-283.
34. Rodgers J., Nicewnader W. Thirteen ways to look at the correlation coefficient. The American Statistician 1998, 42:59-66.
35. Smirniotopoulos J. The new WHO classification of brain tumors. Neuroimaging Clinics of North America 1999, 9:595-613.
36. Vaidyanathanab M., Clarkeab L., Hall L., Heidtmanab C., Velthuizenab R., Goscheab K., Phuphanich S., Greenberg H.W.H., Silbigerab M. Monitoring brain tumor response to therapy using MRI segmentation. Magnetic Resonance Imaging 1997, 15:323-334.
37. Weizman, L., Ben-Sira, L., Joskowicz, L., Precel, R., Constantini, S., Ben-Bashat, D., 2010. Automatic segmentation and components classification of optic pathway gliomas in MRI. In: Proceedings of International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI 2010), vol. LNCS 6361, Beijing, China, pp. 103-110.
38. Weltens C., Menten J., Feron M., Bellon E., Demaerel P., Maes F., den Bogaert W.V., van der Schueren E. Interobserver variations in gross tumor volume delineation of brain tumors on computed tomography and impact of magnetic resonance imaging. Radiotherapy and Oncology 2001, 60:49-59.
39. Zacharaki E.I., Wang S., Chawla S., D S.Y., Wolf R., Melhem E., Davatzikos C. Classification of brain tumor type and grade using MRI texture and shape in a machine learning scheme. Magnetic Resonance in Medicine 2009, 62:1609-1618.
40. Zweig M., Campbell G. Receiver-operating characteristic (ROC) plots: a fundamental evaluation tool in clinical medicine. Clinical Chemistry 1993, 39:561-577.