Abstract
In various clinical or research scenarios, such as neurosurgical intervention planning, diagnostics, or clinical studies concerning neurological diseases, cortex segmentation can be of great value. As, e.g., the visualization of the cortical surface along with target and risk structures enables conservative access planning and gives context information about the patient-specific anatomy. We present an interactive cortex segmentation pipeline (CSP) for T1-weighted MR images, utilizing watershed and level set methods. It is designed to allow the user to adjust the intermediate results at any stage of the segmentation process. Particular attention is paid to the appropriate visualization of the segmentation in the context of the original data for verification and to different interaction methods (manual editing, parameter tuning, morphological operations). Evaluation of the interactive CSP is performed with the Segmentation Validation Engine (SVE) by Shattuck et al. (NeuroImage 45(2):431–439, 2009). The segmentation quality of our method is comparable to the best results of three different established methods: the brain extraction tool (BET), brain surface extractor (BSE), and hybrid watershed algorithm (HWA). Being designed for interaction, the CSP integrates the users’ expertise by allowing him to perform correction at any stage of the pipeline, enabling him to easily achieve a segmentation fulfilling his specific needs.
Similar content being viewed by others
References
Boesen K, Rehm K, Schaper K, Stoltzner S, Woods R, Lüders E, Rottenberg D (2004) Quantitative comparison of four brain extraction algorithms. Neuroimage 22(3):1255–61. http://www.biomedsearch.com/nih/Quantitative-comparison-four-brain-extraction/15219597.html
BrainWeb (2006) Simulated brain database. Available at: http://mouldy.bic.mni.mcgill.ca/brainweb/ (last visited on June 28th 2010)
Cocosco CA, Kollokian V, Kwan RKS, Pike GB, Evans AC (1997) BrainWeb: online interface to a 3D MRI simulated brain database. NeuroImage 5:425
Fennema-Notestine C, Burak Ozyurt I, Clark CP, Morris S, Bischoff-Grethe A, Bondi MW, Jernigan TL, Fischl B, Segonne F, Shattuck DW, Leahy RM, Rex DE, Toga AW, Zou KH, BIRN Morphometry, Brown GG (2006) Quantitative evaluation of automated skull-stripping methods applied to contemporary and legacy images: effects of diagnosis, bias correction, and slice location. Human Brain Mapp 27:99–113
Grau V, Mewes A, Alca M, Kikinis R, Warfield S (2004) Improved watershed transform for medical image segmentation using prior information. IEEE Trans Med Imag 23(4):447–458
Hahn HK, Peitgen HO (2000) The skull stripping problem in MRI solved by a single 3D watershed transform. In: Proc MICCAI. Springer, Berlin, pp 134–143
Hartley SW, Scher AI, Korf ESC, White LR, Launer LJ (2006) Analysis and validation of automated skull stripping tools—a validation study based on 296 MR images from the Honolulu Asia aging study. NeuroImage 30(4):1179–1186
Hohne KH, Hanson WA (1992) Interactive 3D segmentation of MRI and CT volumes using morphological operations. J Assist Tomogr 16(2):285–294
IBSR (2009) Internet brain segmentation repository. Available at: http://www.cma.mgh.harvard.edu/ibsr/ (last visited on June 28th 2010)
John C, Kevin W, Emma L, Chao C, Barbara P, Declan J (2007) Statistical morphological skull stripping of adult and infant MRI data. Comput Biol Med 37(3):342–357
Kotani K, Horii K (2003) An analysis of muscular load and performance in using a pen-tablet system. J Physiol Anthropol Appl Hum Sci 22(2):89–95
Olabarriaga SD, Smeulders AWM (1997) Setting the mind for intelligent interactive segmentation: overview, requirements, and framework. In: Proc IPMI. Springer, Berlin, pp 417–422
Park JG, Lee C (2009) Skull stripping based on region growing for magnetic resonance brain images. NeuroImage 47(4):1394–1407
Perona P, Malik J (1990) Scale-space and edge detection using anisotropic diffusion. IEEE Trans Pattern Anal Mach Intell 12(7):629–639
Pfeifle M, Born S, Fischer J, Duffner F, Hoffmann J, Bartz D (2007) VolV—eine OpenSource-Plattform für die medizinische Visualisierung. In: Proc CURAC, pp 193–196
Segonne F, AM Dale, Busa E, Glessner M, Salat D, Hahn HK, Fischl B (2004) A hybrid approach to the skull stripping problem in MRI. NeuroImage 22:1060–1075
Shattuck DW, Sandor-Leahy SR, Schaper KA, Rottenberg DA, Leahy RM (2001) Magnetic resonance image tissue classification using a partial volume model. NeuroImage 13(5):856–876
Shattuck DW, Prasad G, Mirza M, Narr KL, Toga AW (2009) Online resource for validation of brain segmentation methods. NeuroImage 45(2):431–439
Sijbers J, Scheunders P, Verhoye M, Linden AVD, Dyck DV, Raman E (1997) Watershed-based segmentation of 3D MR data for volume quantization. Magn Reson Imaging 15(6):679–688
Smith SM (2002) Fast robust automated brain extraction. Hum Brain Mapp 17(3):143–155
SVE (2009) Segmentation validation engine. Available at: http://sve.loni.ucla.edu/ (last visited on June 28th 2010)
Warfield SK, Zou KH, Wells WM (2004) Simultaneous truth and performance level estimation (STAPLE): an algorithm for the validation of image segmentation. IEEE Trans Med Imag 23:903–921
Wellein D, Pfeifle M, Althuizes M, Voitel L, Bartz D (2010) A cortex segmentation pipeline. In: Proc BVM, pp 271–275
Yoo TS, Ackerman MJ, Lorensen WE, Schroeder W, Chalana V, Aylward S, Metaxas D, Whitaker R (2002) Engineering and algorithm design for an image processing API: a technical report on ITK—the insight toolkit. In: Proc medicine meets virtual reality, pp 586–592
AH Zhuang, Valentino D, Toga A (2006) kSull—stripping magnetic resonance brain images using a model-based level set. Neuroimage 32(1):79–92
Zu Y, Guang H, Jing Z (2002) Automated histogram-based brain segmentation in T1-weighted three-dimensional magnetic resonance head images. NeuroImage 17(3):1587–1598
Author information
Authors and Affiliations
Corresponding author
Rights and permissions
About this article
Cite this article
Wellein, D., Born, S., Pfeifle, M. et al. A pipeline for interactive cortex segmentation. Comput Sci Res Dev 26, 87–96 (2011). https://doi.org/10.1007/s00450-010-0130-4
Published:
Issue Date:
DOI: https://doi.org/10.1007/s00450-010-0130-4