Automatic Brain Segmentation in Time-of-Flight MRA Images

Summary

Objectives: Cerebral vascular malformations might, caused by ruptures, lead to strokes. The rupture risk depends to a great extent on the individual anatomy of the vasculature. The 3D Time-of-Flight (TOF) MRA technique is one of the most commonly used non-invasive imaging techniques to obtain knowledge about the individual vascular anatomy. Unfortunately TOF images exhibit drawbacks for segmentation and direct volume visualization of the vasculature. To overcome these drawbacks an initial segmentation of the brain tissue is required.

Methods: After preprocessing of the data is applied the low-intensity tissues surrounding the brain are segmented using region growing. In a following step this segmentation is used to extract supporting points at the border of the brain for a graph-based contour extraction. Finally a consistency check is performed to identify local outliers which are corrected using non-linear registration.

Results: A quantitative validation of the method proposed was performed on 18 clinical datasets based on manual segmentations. A mean Dice coefficient of 0.989 was achieved while in average 99.56% of all vessel voxels were included by the brain segmentation. A comparison to the results yielded by three commonly used tools for brain segmentation revealed that the method described achieves better results, using TOF images as input, which are within the inter-observer variability.

Conclusion: The method suggested allows a robust and automatic segmentation of brain tissue in TOF images. It is especially helpful to improve the automatic segmentation or direct volume rendering of the cerebral vascular system.

Keywords

Computer-assisted image processing - brain vascular disorders - brain - image segmentation - magnetic resonance angiography

• References

• 1 Davis W, Warnock S, Harnsberger H, Parker D, Chen C. Intracranial MRA: single volume vs. multiple thin slab 3D time-of-flight acquisition. J Comput Assist Tomogr 1993; 17 (01) 15-21.
• 2 Forkert ND, Säring D, Fiehler J, Illies T, Möller D, Handels H. Analysis and Dynamic 3D Visualization of Cerebral Blood Flow Combining 3D and 4D MR Image Sequences. Proceedings of SPIE Medical Imaging 2009 Vol. 7261: 33-1–33–8.
• 3 Wilson D, Noble J. An adaptive segmentation algorithm for time-of-flight MRA data. IEEE Trans Med Imaging 1999; 18 (10) 938-945.
• 4 Hassouna MS, Farag A, Hushek S, Moriarty T. Cerebrovascular segmentation from TOF using stochastic models. Med Image Anal 2006; 10 (01) 2-18.
• 5 Chapman B, Stapelton J, Parker D. Intracranial vessel segmentation from time-of-flight MRA using pre-processing of the mip z-buffer: accuracy of the zbs algorithm. Med Image Anal 2004; 8 (02) 113-126.
• 6 Fiehler J, Illies T, Piening M, Säring D, Forkert ND, Grzyska U. et al. Territorial and Microvascular Perfusion Impairment in Brain Arteriovenous Malformations. AJNR Am J Neuroradiol 2009; 30: 356-361.
• 7 Fischl B, Salat DH, Busa E, Albert M, Dieterich M, Haselgrove C. et al. Whole brain segmentation: Automated labeling of neuroanatomical structures in the human brain. Neuron 2002; 33: 341-355.
• 8 Iosifescu DF, Shenton ME, Warfield SK, Kikinis R, Dengler J, Jolesz FA. et al. An automated registration algorithm for measuring MRI subcortical brain structures. Neuroimage 1997; 6: 13-25.
• 9 Ward BD. Intracranial segmentation. Technical report 1999; Biophysics Research Institute Medical College of Wisconsin.
• 10 Lemieux L, Hagemann G, Krakow K, Woermann FG. Fast, accurate, and reproducible automatic segmentation of the brain in T1-weighted volume MRI data. Magn Reson Med 1999; 42: 127-135.
• 11 Hahn HK, Peitgen HO. The Skull Stripping Problem in MRI Solved by a Single 3D Watershed Transform. MICCAI 2000 pp 134-143.
• 12 Shattuck D, Sandor-Leahy S, Schaper K, Rottenberg D, Leahy R. Magnetic resonance image tissue classification using a partial volume model. Neuroimage 2003; 13: 856-876.
• 13 Smith S. Fast robust automated brain extraction. Hum Brain Mapp 2002; 17 (03) 143-155.
• 14 Dale AM, Fischl B, Sereno MI. Cortical surfacebased analysis I. Segmentation and surface reconstruction. Neuroimage 1999; 9 (02) 179-194.
• 15 Rifai H, Bloch I, Hutchinson S, Wiart J, Garnero L. Segmentation of the skull in MRI volumes using deformable model and taking the partial volume effect into account. Med Image Anal 2000; 4 (03) 219-233.
• 16 Kapur T, Grimson WEL, Wells III WM, Kikinis R. Segmentation of Brain Tissue from Magnetic Resonance Images. Med Image Anal 1996; 1 (02) 109-127.
• 17 Ségonne F, Dale AM, Busa E, Glessner M, Salat D, Hahn HK. et al. A hybrid approach to the skull stripping problem in MRI. Neuroimage 2004; 22: 1060-1075.
• 18 Fennema-Nostine C, Burak Ozyurt I, Clark CP, Morris S, Bischoff-Grethe A, Bondi MW. et al. Quantitative evaluation of automated skull-stripping methods applied to contemporary and legacy images: Effects of diagnosis, bias correction, and slice location. Hum Brain Mapp 2006; 27 (02) 99-113.
• 19 Kholmovski E, Alexander A, Parker D. Correction of slab boundary artifact using histogram matching. J Magn Reson Imaging 2002; 15: 610-617.
• 20 Whitaker R, Xue X. Variable-conductance, level-set curvature for image denoising. Proceeding of International Conference on Image Processing 2001 pp 142-145.
• 21 Barret W, Mortensen E. Interactive livewire boundary extraction. Med Image Anal 1997; 1 (04) 331-341.
• 22 Knig S, Hesser J. Live-Wires Using Path-Graphs. Methods Inf Med 2004; 43: 371-375.
• 23 Färber M, Ehrhardt J, Handels H. Live-Wire-based segmentation using similarities between corresponding image structures. Comput Med Imaging Graph 2007; 31 (07) 549-561.
• 24 Deriche R. Using Canny’s criteria to derive a recursively implemented optimal edge detector. Int J Computer Vis 1987; 1 (02) 167-187.
• 25 Dijkstra E. A note on two problems in connexion with graphs. Numerische Mathematik 1959; 1: 269-271.
• 26 Thirion JP. Image matching as a diffusion process: an analogy with maxwell’s demons. Med Image Anal 1998; 2 (03) 243-260.
• 27 Säring D, Fiehler J, Forkert N, Piening M, Handels H. Visualization and analysis of cerebral arteriovenous malformation combining 3D and 4D MR image sequences. Int J CARS 2007; 2 (01) 75-79.