Partitioning of the brain using graph cut

Document Sample
Partitioning of the brain using graph cut Powered By Docstoc
					                                                          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: robert.dahnke@uni-jena.de                                                                                           PDF of the poster is available at: http://dbm.neuro.uni-jena.de/HBM2010/Dahnke.pdf

				
DOCUMENT INFO
Shared By:
Categories:
Tags:
Stats:
views:9
posted:8/18/2011
language:English
pages:1