메뉴 건너뛰기




Volumn , Issue , 2011, Pages 1733-1736

Longitudinal assessment of brain tumors using a repeatable prior-based segmentation

Author keywords

brain tumor; follow up; MRI; segmentation

Indexed keywords

AUTOMATIC METHOD; BRAIN TUMORS; DATA SETS; FOLLOW-UP; MANUAL SEGMENTATION; OBSERVER VARIABILITY; TUMOR BOUNDARY;

EID: 80055038191     PISSN: 19457928     EISSN: 19458452     Source Type: Conference Proceeding    
DOI: 10.1109/ISBI.2011.5872740     Document Type: Conference Paper
Times cited : (2)

References (14)
  • 1
    • 13844281110 scopus 로고    scopus 로고
    • A system for brain tumor volume estimation via MR imaging and fuzzy connectedness
    • J. Liu, J.K. Udupa, D. Odhner, D. Hackney and G. Moonis. A system for brain tumor volume estimation via MR imaging and fuzzy connectedness. Comput. Med. Imag. Graphics 29(1):21-34, 2005.
    • (2005) Comput. Med. Imag. Graphics , vol.29 , Issue.1 , pp. 21-34
    • Liu, J.1    Udupa, J.K.2    Odhner, D.3    Hackney, D.4    Moonis, G.5
  • 2
    • 0031777574 scopus 로고    scopus 로고
    • Intra and interobserver variability in contouring prostate and seminal vesicles: Implications for conformal treatment planning
    • C. Fiorino, M. Reni, M. Bolognesi, G. Cattaneo, R. Calandrino. Intra and interobserver variability in contouring prostate and seminal vesicles: implications for conformal treatment planning. Radiother. Oncol. 47: 285-292, 1998.
    • (1998) Radiother. Oncol. , vol.47 , pp. 285-292
    • Fiorino, C.1    Reni, M.2    Bolognesi, M.3    Cattaneo, G.4    Calandrino, R.5
  • 3
    • 0035400593 scopus 로고    scopus 로고
    • Interobserver variations in gross tumor volume delineation of brain tumors on computed tomography and impact of magnetic resonance imaging
    • C. Weltens, J. Menten, M. Feron, E. Bellon, P. Demaerel, F. Maes, W. Van den Bogaert, E. van der Schueren. Interobserver variations in gross tumor volume delineation of brain tumors on computed tomography and impact of magnetic resonance imaging. Radiother. Oncol. 60:49-59, 2001.
    • (2001) Radiother. Oncol. , vol.60 , pp. 49-59
    • Weltens, C.1    Menten, J.2    Feron, M.3    Bellon, E.4    Demaerel, P.5    Maes, F.6    Van Den Bogaert, W.7    Van Der Schueren, E.8
  • 4
    • 43049179622 scopus 로고    scopus 로고
    • Efficient multilevel brain tumor segmentation with integrated Bayesian model classification
    • J.J. Corso, E. Sharon, S. Dube, S. El-Saden, U. Sinha and A. Yuille. Efficient multilevel brain tumor segmentation with integrated bayesian model classification. IEEE Trans. Medical Imaging 27(5):629-640, 2008.
    • (2008) IEEE Trans. Medical Imaging , vol.27 , Issue.5 , pp. 629-640
    • Corso, J.J.1    Sharon, E.2    Dube, S.3    El-Saden, S.4    Sinha, U.5    Yuille, A.6
  • 5
    • 33751563521 scopus 로고    scopus 로고
    • Level set evolution with region competition: Automatic 3D segmentation of brain tumors
    • Quebec, Canada, August
    • S. Ho, E. Bullitt and G. Gerig. Level set evolution with region competition: automatic 3D segmentation of brain tumors. In Int. Conf. Pattern Recognit., Quebec, Canada, August 2002, pp. 532-535.
    • (2002) Int. Conf. Pattern Recognit. , pp. 532-535
    • Ho, S.1    Bullitt, E.2    Gerig, G.3
  • 6
    • 0347252327 scopus 로고    scopus 로고
    • Automatic brain tumor segmentation by subject specific modification of atlas priors
    • M. Prastawa, E. Bullitt, N. Bullitt, K.V. Leemput and G. Gerig. Automatic brain tumor segmentation by subject specific modification of atlas priors. Acad. Rad. 10:1341-1348, 2003.
    • (2003) Acad. Rad. , vol.10 , pp. 1341-1348
    • Prastawa, M.1    Bullitt, E.2    Bullitt, N.3    Leemput, K.V.4    Gerig, G.5
  • 7
    • 0032701497 scopus 로고    scopus 로고
    • The new WHO classification of brain tumors
    • J.G. Smirniotopoulos. The new WHO classification of brain tumors. Neuroimag. Clinics North Amer. 9(4): 595-613, 1999.
    • (1999) Neuroimag. Clinics North Amer. , vol.9 , Issue.4 , pp. 595-613
    • Smirniotopoulos, J.G.1
  • 8
    • 33646709406 scopus 로고    scopus 로고
    • Segmenting brain tumor with conditional random fields and support vector machines
    • Beijing, China, October
    • C.H. Lee, M. Schmidt, A. Murtha, A. Bistritz, J. Sander and R. Greiner. Segmenting brain tumor with conditional random fields and support vector machines. In Proc. Int. Conf. Comput. Vision, Beijing, China, October 2005, pp. 469-478.
    • (2005) Proc. Int. Conf. Comput. Vision , pp. 469-478
    • Lee, C.H.1    Schmidt, M.2    Murtha, A.3    Bistritz, A.4    Sander, J.5    Greiner, R.6
  • 10
    • 0032587462 scopus 로고    scopus 로고
    • Segmentation of magnetic resonance images using fuzzy algorithms for learning vector quantization
    • N.B. Karayiannis and P.I. Pai. Segmentation of magnetic resonance images using fuzzy algorithms for learning vector quantization. IEEE Trans. Med. Imag. 18(2): 172-180, 1999.
    • (1999) IEEE Trans. Med. Imag. , vol.18 , Issue.2 , pp. 172-180
    • Karayiannis, N.B.1    Pai, P.I.2
  • 11
    • 78349278060 scopus 로고    scopus 로고
    • Automatic segmentation and components classification of optic pathway gliomas
    • MRI. T. Jiang et. al. (Eds.) Springer LNCS 6361
    • L. Weizman, L. Ben-Sira, L. Joskowicz, R. Precel, S. Constantini and D. Ben-Bashat. Automatic segmentation and components classification of optic pathway gliomas in MRI. T. Jiang et. al. (Eds.): MICCAI 2010, Part I, Springer LNCS 6361:103-110, 2010.
    • (2010) MICCAI 2010, Part I , pp. 103-110
    • Weizman, L.1    Ben-Sira, L.2    Joskowicz, L.3    Precel, R.4    Constantini, S.5    Ben-Bashat, D.6


* 이 정보는 Elsevier사의 SCOPUS DB에서 KISTI가 분석하여 추출한 것입니다.