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Partitioning of the brain using graph-cut Robert Dahnke, Rachel Aine Yotter, Christian Gaser Structural Brain Mapping Group, Department of Psychiatry Friedrich-Schiller-University of Jena, Germany INTRODUCTION RESULTS Skull-stripping and partitioning in major regions are important tasks for further analysis of MRI data We test our method for normal adult and child brains, but also for extremely deformed brains with that still allows improvements (Han et al., 2007; Sadananthan et al. 2010; Zhao et al, 2010). We have large ventricles. Figure 2 shows slices of di erent subjects with the T1-data as background and the developed a new method to re ne the SPM segmentation (Ashburner et al., 2005) by a improved personal atlas SLAB as colored overlay. The SEGPF map was used to generate surfaces with PBT removal of non-brain tissues, and to separate the brain into major regions. The method uses atlas, (Dahnke et al. 2010). Figure 3 shows improvements of our method compared to VBM8. distance and intensity information, and was validated for normal and abnormal brains of subjects with di erent age. Child Adult Old Patient METHODS As input, the label map SEG and the anatomical bias corrected T1 image of the VBM8 toolbox (http://dbm.neuro.uni-jena.de) of SPM8 (Ashburner et al., 2005) are required (Figure 1). Dartel normalization (Ashburner et al., 2007) is used to project the atlas map ALAB onto subject space. It allows identi cation of major WM regions, and creation of an individual atlas map SLAB. After this rst alignment, a graph-cut algorithm is used to allocate GM/WM voxels around these AC+20 mm regions. To remove blood vessels and meninges the graph-cut of cerebral structures only allows small increases of the image intensity for clustered of neighbors to the start voxels. Remaining voxels are set to GM if they are close to the WM or added as blood vessel to the SLAB map. For further blood vessel detection, morphological operations, distance, and thickness information are used. To nd the ventricle, the bottleneck method (Mangin et al., 1996) is used. CSF voxels in the ventricular ALAB regions get a high potential, whereas voxels that belong to the background, cerebellum or brainstem represent low potential. The Laplace equation is solved within the CSF with Dirichlet boundary conditions. The biggest left and right component of voxels with more than 70% of the high potential were added as ventricle to the SLAB map. For subcortical structures, GM and WM voxels of subcortical and ventricular ALAB regions get high potential, whereas non-ventricular CSF is used as the negative potential. A threshold of 95% for AC-20 mm the high potential is used to locate subcortical regions in the GM and update the SLAB map. Figure 2: Volumes of a healthy child (far left), a healthy adult (middle left), a healthy old (middle right), and a patient Volumes of dinormal child (far left), row: AC+20 mm,(middle left), :aAC-20 mm). (middle right), Fig. 2: (far right) at a erent axial levels (top a normal adult bottom row normal old For surface reconstruction, ventricles and subcortical structures are lled and non-brain tissues and a patient (far right) at di erent axial levels (top row: AC+20 mm, bottom row : AC-20 mm). are removed, resulting in a map SEGPF. Morphological operations are used in the neighborhood The Segmentation Validation Engine (Shattuck et of the corrected regions to allow continuous structures. al. 2009) was used for further evaluation (Figure Finally, the graph-cut algorithm divides each region into its left and right component, starting 4). Because the manual tracing method of blood from the highest tissue class in SEGPF. vessels, meninges and CSF is not fully clear an interpretation of the results is problematic. In (Rehm Input images: T1 image, VBM8 segment et al., 2003) two experts created tracings with and map SEG and atlas map ALAB. without blood vessels, leading to big visible results in the volume rendering, but only small di erences in Dice similarity (below 0.02). T1 SEG ALAB Calculation times are around 8 minutes on a 2.4 GHz iMac. Creation of TSEG image as a combination of the T1 and SEG image. Standard VBM8 segmentation (left) contains more blood vessels (manually colored) than our method DARTEL projection of the atlas map to (right). Further colors on the right surface indicate the subject space. TSEG di erent regions. Results for Study #221: Identi cation of main structures in the TSEG Dice coe cient 0.9706 SVE sesults for study #221: ± 0.0041 images with help of atlas information. Jaccard index 0.9430 ± 0.0076 Dice coe cient 0.9706 ± 0.0041 Figure 3: Standard VBM8 segmentation (left) contains Fig. 3: Standard VBM8 segmentation (left) contains more blood vessels (manually colored) than our Alignment of WM tissue with graph-cut. Sensitivity Jaccard index 0.9542 ± 0.0105 0.9430 ± 0.0076 more blood vessels (manually colored) than our method (right). Further colors on the right surface indicate the di erent regions. Speci city Sensitivity 0.9979 ± 0.0009 0.9542 ± 0.0105 method (right). Further colors on the right surface Estimation of GM tissues around the main Speci city 0.9979 ± 0.0009 indicate the di erent regions. structures with graph-cut. Detection of blood vessels and meninges. Average False Positive 15.8 11.1 7.4 Identi cation of the ventricle and deep GM structures, and alignment of CSF. Closing of the ventricle and deep GM structures for later surface reconstruction. Removal of blood vessels and meninges. Morphological operations for continuous 0 0 0 structures. Average False Negative 37.4 25.2 24.3 Bilateral alignment of all structures using graph-cut. SEGC SEGPF SLAB Figure 1: Algorithm ow diagram input and and output images and sub-steps) Fig. 1: Algorithm ow diagram (with (with input output images and sub-steps) 0 0 0 REFERENCES Figure 3: Shown are results for 40 subjects processed using the SVE (Segmentation Validation Engine). More Ashburner, J. (2005), ‘Uni ed segmentation’, Neuroimage, vol. 26 (3) pp. 839-51 detailedFig. 4: Shown are results for 40 subjects processed using the SVE (Segmentation Validation Engine). results are available at http://sve.loni.ucla.edu/archive/study/?id=221. More detailed results are available at http://sve.loni.ucla.edu/archive/study/?id=221. Ashburner, J. (2007), ‘A fast di eomorphic image registration algorithm’, Neuroimage, vol. 38 (1) pp. 95-113 Dahnke, R. (2010), ‘Central Surface Reconstruction using a Projection Scheme’, HBM 2010 Han, X. (2007) ‘Atlas renormalization for improved brain MR image segmentation across scanner platforms’, CONCLUSIONS IEEE transactions on medical imaging, vol. 26 (4) pp. 479-86 The new introduced method is able to improve the results of the default SPM8 segmentation Liang, L. (2007), ‘Automatic segmentation of left and right cerebral hemispheres from MRI brain volumes and allows the separation of the brain into di erent atlas-based regions. Due to the DARTEL using the graph cut algorithm’, Neuroimage, vol. 34 (3) pp. 1160-1170 normalization, regional alignments have a high accuracy. Compared to Zhao et al., 2010, and Mangin, F.R. (1996), ‘Shape Bottlenecks and Convservative Flow’, IEEE Work. MMBIA, San Francisco, CA, pp. 319–328. Liang et al., 2007, it allows the alignment of major structures, as well as the correction for blood Rehm, K. (2004), ‘Putting our heads together: a consensus approach to brain/non-brain segmentation in T1- vessels and meninges and lling of subcortical structures and ventricles. weighted MR volumes’, Neuroimage, vol. 22 (3) pp. 1262-70 Sadananthan, S.A. 2010, ‘Skull stripping using graph cuts’, Neuroimage, vol. 49 (1) pp. 225-39 Shattuck, D.W. (2009), ‘Online resource for validation of brain segmentation methods’, Neuroimage, vol. 45 (2) pp. 431-9. ACKNOWLEDGEMENTS Zhao, L. (2010), ‘Automatic cerebral and cerebellar hemisphere segmentation in 3D MRI: Adaptive disconnection algorithm’, Medical Image Analysis, vol. 14 (3) pp. 360-72. R.D, R.Y., & C.G. are supported by the German BMBF grants 01EV0709 & 01GW0740. E-mail: email@example.com PDF of the poster is available at: http://dbm.neuro.uni-jena.de/HBM2010/Dahnke.pdf
"Partitioning of the brain using graph cut"