Transcript No Slide Title

Corpus Callosum Probabilistic Subdivision
based on Inter-Hemispheric Connectivity
Martin Styner1,2, Ipek Oguz1, Rachel Gimpel Smith2, Carissa Cascio2, Matthieu Jomier1
1
Department of Computer Science, University of North Carolina at Chapel Hill,
2 Department of Psychiatry, University of North Carolina at Chapel Hill
Shape analysis and Diffusion Tensor Image (DTI) tractography have become of increasing interest in neuroimaging. Here both are employed to
compute a probabilistic subdivision model of the Corpus Callosum (CC) structure. The CC subdivision allows us to study the regional CC morphology
regarding area measurements or Diffusion Tensor Imaging properties. As a first small scale application, we applied it to a small study of regional CC
growth in pediatric healthy controls.
Introduction
The corpus callosum (CC) is the major commissural pathway between
the hemispheres and plays an integral role in relaying sensory, motor
and cognitive information from homologous region in the two
hemispheres. It is of much interest in neuroimaging studies of normal
development, schizophrenia and autism. The computation of CC regional
areas is most commonly executed manually by relabeling an already
segmented structure into subregions. These manual methods are timeconsuming, not reproducible and subjective. The currently most widely
applied subdivision scheme for the CC was originally proposed by
Witelson[4] and is motivated by neuro-histological studies. We propose a
novel automatic CC subdivision based on probabilistic boundaries based
on inter-hemispheric connectivity from Diffusion Tensor Imaging[1] (DTI).
Fig. 1: Corpus callosum in an MR
image
(left)
with
Witelson
subdivision[5]
and
its
neurohistological motivation (right). This
subdivision scheme is sensitive to
alignment and/or manual labeling.
Fig. 3: Left: Fibers of 4 selected lobes transformed back to MR image space. Middle: Schematic visualization of
probability computation. Right: Contour probability maps of 5 training cases.
Results
The probability maps of all 5 cases in the training population show a
high similarity across all cases and the subdivision model. The largest
variability is present in the occipital-temporal lobe section. Alternatively,
we also computed the hard decision maps, which resulted in a high
decision variability in all cases and the final probability map. The
application of the subdivision model shows that the occipital-temporal
lobe region has a low probability in a relatively large region. The
resulting probabilistic area is relatively large (21% for the shown case).
A hard decision model would highly underestimate this area.
Methods/Data/Material
Our subdivision is based on a training population of 5 pediatric cases
(age 2-4y). We first compute for each case the automatic lobe
subdivision and CC segmentation. The CC is segmented as a 2D
contour on the midsagittal plane based on a deformable shape model
trained on over 200 cases (both adult and pediatric). The lobe
subdivision uses a fluid deformable registration of a pediatric lobe atlas
to all cases
Average Probabilistic Contour Model
Fig. 4: Left: Final probabilistic subdivision model. Right: Sample subdivision case with relative area noted below.
The subdivision was applied to a small study of CC growth in 3 healthy
subjects from age 2 to 4. The results show CC growth over its full length,
and its main growth in the region associated with anterior frontal lobe
connections. This region experienced a relative growth of 26% (posteriorfrontal:24%, parietal:15%, occipital-temporal:15%).
Conclusions
Fig. 2: Left: Midsagittal MR slices and the automatically segmented corpus callosum using deformable shape
models. Middle: 3D view of the lobe subdivision (yellow=frontal, purple=parietal, red=occipital, green=temporal
lobe). Right: Set of inter-hemispheric connectivity from a sample DTI dataset.
In-vivo assessment of the inter-hemispheric pathways through the CC is
difficult, but can be approximated using DTI and Tractography[2,3]. The
lobe subdivision serves as an initialization for the DTI Tractography. This
leads to a set of inter-hemispheric DTI fiber tracts for each lobe set. In
the next step we compute a distance-weighted probabilistic subdivision
of the CC contour from the location of all tracts. The resulting
subdivisions are averaged to produce the final CC subdivision model
consisting of probabilistic contour maps that assign to each contour
point the probabilities to belong to any of the connectivity based
subdivisions. The probabilities are propagated to the whole CC object
using a Danielsson distance transform based label map. Our method is
fully automatic and its results are more stable than commonly applied
schemes such as the Witelson subdivision.
We developed a novel probabilistic CC subdivision based on interhemispheric connectivity. Applied to a study of healthy growth from age 2
to 4, we showed that the main growth is in CC regions associated with
frontal lobe connections.
Fig. 5:Relative growth curves of CC subdivision regions. Data from 3 healthy subjects along mean curves from age 2
to age 4. A: Regional growth relative to the overall CC growth. B: Regional growth relative to the size of the
corresponding region at age 2.
Original brain images for the corpus callosum were provided by BIOMORPH consortium (EU BIOMED 2) and the UNC Autism center
REFERENCES
[1]. Fillard, P., Gilmore, J., Lin, W., Piven, J., Gerig, G.: Quantitative analysis of white matter fiber properties along geodesic paths. MICCAI. 2879 in Lecture Notes in Computer Science (2003) 16–23
[2]. Xu, D., Mori, S., Solaiyappan, M., van Zijl, P., Davatzikos, C.: A framework for callosal fiber distribution analysis. NeuroImage 17 (2002) 11311143
[3]. Gee, J., Zhang, H., Dubb, A., Avants, B., Yushkevich, P., Duda, J.: Anatomy-based visualizations of diffusion tensor images of brain white matter. Vis. and Image Processing of Tensor Fields. (2005)
[4]. Witelson, S.: Hand and sex differences in the isthmus and genu of the human corpus callosum. a postmortem morphological study. Brain 3 (1989) 799–835
[5]. Narr, K., Thompson, P., Sharma, T., Moussai, J., Cannestra, A., Toga, A.: Mapping morphology of the corpus callosum in schizophrenia. Cereb Cortex 1 (2000) 40–9
May 2005, UNC Radiology Symposium