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SP-MORPH |
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Spectral Mesh Processing for
Craniofacial Dysmorphology |
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This project addresses the analysis of facial
geometry for the quantification of craniofacial Dysmorphology, motivated by:
1) its association with, and ability to inform on, diseases of early brain
development, such as Down syndrome, fetal alcohol syndrome and schizophrenia;
2) increasing availability of three-dimensional (3D) imaging technologies
that overcome many of the limitations inherent to two-dimensional approaches.
We focus on
the development of algorithms for automated and highly accurate analysis of
facial surfaces in 3D, with special interest in techniques based on spectral
decomposition methods. As opposed to traditional methods, based on a reduced
set of landmark points, spectral mesh processing (SMP) allows analysis of the
whole facial surface. Briefly speaking, SMP algorithms provide a
decomposition of the geometry into its natural vibration modes. The resulting
components, analogous to the Fourier Transform for 1D signals, are linked to
intrinsic properties of the object, such as (a)symmetry,
believed to be a crucial component of dysmorphology. While SMP is a novel and
very active trend in computer graphics and vision, it still involves a number
of important technical challenges for its use in engineering applications,
where input data would usually need to undergo one or more pre-processing
steps, often with the need for human intervention before such spectral
methods can be used. The accuracy
and precision of the algorithms used for geometric processing play a crucial
role in the project given the interest in neuropsychiatric disorders, where
craniofacial dysmorphology is considerably more subtle than, for example, in
Down syndrome. We investigate the decomposition of 3D mesh geometries in
general from a theoretical perspective, while taking advantage of the special
characteristics of facial geometry (which allow, still with spectral methods,
its mapping in 2D with very little distortion) to validate the 3D methods
against results in the 2D domain, currently better understood. |
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Craniofacial Dysmorphology |
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The relation
between craniofacial dysmorphology and certain diseases had been already suggested
many years ago. Among the most evident examples are the distinctive facial
characteristics of patients with Down syndrome [Ferrario 2005],
but these have also been identified in autism [Ozgen 2010],
schizophrenia [Hennessy 2007], bipolar disorder
[Hennessy 2010], velocardiofacial
syndrome [Óskarsdóttir 2005], fetal alcohol syndrome, etc. In the
latter, the unique pattern of facial anomalies is the only diagnostic feature
so far that is specific to the condition and has therefore been the focus of
case definition [Mutsvangwa 2010]. New insights
in developmental biology indicate a deep intimacy in morphogenesis of certain
regions of the brain and the face, and a rapidly increasing number of genes
have been identified as regulating cerebro-craniofacial
development [Helms 2005]. As a concrete example, the forebrain, which acts as
the supporting framework for facial morphogenesis, has been shown to provide
signals with instructional information for elaborating the proximodistal and mediolateral
axes of the middle and upper facial skeleton [Marcucio 2005].
This explains why disruptions to the early development of certain regions of
the brain are accompanied by craniofacial dysmorphology. Based on the
above evidence, craniofacial geometry has been suggested as a potential index
of early developmental disturbance [Hammond 2007,
Hennessy 2010, Chakravarty 2011]. Recent technological advances on 3D
imaging have made it possible to analyze craniofacial shape based on indirect
measurements, as opposed to the classical direct anthropometry. However, in
contrast to the evident dysmorphology in diseases like Down syndrome,
dysmorphology in other disorders such as schizophrenia, bipolar disorder and velocardiofacial syndrome, can be very subtle to the
extent that it can hardly be identified by the human eye. Therefore, we
addressed the issues of highly accurate and fully automatic analysis of
craniofacial geometry, with a special emphasis on the repeatability of
results, so that large populations could be analyzed in a systematic and
consistent manner. The activity
in this project can be roughly subdivided into 4 lines that have advanced in
parallel: ·
Automatic
landmark localization ·
Landmark-based
surface normalization |
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References |
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[Chacravarty 2011] M.M. Chakravarty et al. Automated analysis of craniofacial
morphology using magnetic resonance images. Plos ONE
6(5):e20241, 2011. [Ferrario 2005] V.F. Ferrario, C. Dellavia, G. Serrao and C. Sforza. Soft tissue facial angles in Down’s syndrome
subjects: a three-dimensional non-invasive study.
European Journal of Orthodontics 27(4):355–62, 2005 [Hammond 2007] P. Hammond. The use of 3D face
shape modelling in dysmorphology. Archives of Disease in Childhood
92:1120–1126, 2007. [Hennessy 2007] R.J. Hennessy, P.A.
Baldwin, D.J. Browne, A. Kinsellac and J.L. Waddingtona. Three-dimensional
laser surface imaging and geo- metric morphometrics resolve frontonasal dysmorphology in schizophrenia.
Biological Psychiatry 61:1187–1194, 2007. [Hennessy 2010] R.J. Hennessy, P.A.
Baldwin, D.J. Browne, A. Kinsellac and J.L. Waddingtona. Frontonasal dysmorphology in bipolar disorder by 3D laser
surface imaging and geometric morphometrics: Comparisons with schizophrenia.
Schizophrenia Research,
122(1-3):63–71, 2010. [Marcicio 2005] R.S. Marcucio,
D.R. Cordero, D. Hu and J.A. Helms. Molecular
interactions coordinating the development of the forebrain and face.
Developmental Biology, 284:48–61, 2005. [Mutsvangwa 2010] T.E.M. Mutsvangwa,
E.M. Meintjes, D.L. Viljoen
and T.S. Douglas. Morphometric analysis and classification of the facial
phenotype associated with Fetal Alcohol Syndrome in
5- and 12-year-old children. American Journal of Medical Genetics
Part A. 152A:32–41, 2010. [Óskarsdóttir 2005] S. Óskarsdóttir,
C. Persson, B.O. Eriksson and A. Fasth. Presenting
phenotype in 100 children with the 22q11 deletion syndrome. European
Journal of Pediatrics.
164(3):146–153, 2005. [Ozgen 2010] H.M. Ozgen,
J.W. Hop, J.J. Hox, F.A Beemer and H van Engeland,. Minor
physical anomalies in autism: a meta-analysis. Molecular Psychiatry,
15:300–307, 2010. |
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Funding |
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The SP-MORPH
project is funded by a Marie Curie Intra-European Fellowship (IEF) from the
7th Framework Program of the European Commission (Project Number 299605).
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