Imaging in Systems Biology

Sean G. Megason^{1, ,} and Scott E. Fraser^{1, ,}

¹Beckman Institute and Division of Biology, California Institute of Technology, Pasadena, CA 91125, USA

Available online 6 September 2007.

Most systems biology approaches involve determining the structure of biological circuits using genomewide “-omic” analyses. Yet imaging offers the unique advantage of watching biological circuits function over time at single-cell resolution in the intact animal. Here, we discuss the power of integrating imaging tools with more conventional -omic approaches to analyze the biological circuits of microorganisms, plants, and animals.

(link)

Sparse Decomposition and Modeling of Anatomical Shape Variation

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Sparse Decomposition and Modeling of Anatomical Shape Variation

via Medical Imaging, IEEE Transactions on - new TOC on 12/3/07

Recent advances in statistics have spawned powerful methods for regression and data decomposition that promote sparsity, a property that facilitates interpretation of the results. Sparse models use a small subset of the available variables and may perform as well or better than their full counterparts if constructed carefully. In most medical applications, models are required to have both good statistical performance and a relevant clinical interpretation to be of value. Morphometry of the corpus callosum is one illustrative example. This paper presents a method for relating spatial features to clinical outcome data. A set of parsimonious variables is extracted using sparse principal component analysis, producing simple yet characteristic features. The relation of these variables with clinical data is then established using a regression model. The result may be visualized as patterns of anatomical variation related to clinical outcome. In the present application, landmark-based shape data of the corpus callosum is analyzed in relation to age, gender, and clinical tests of walking speed and verbal fluency. To put the data-driven sparse principal component method into perspective, we consider two alternative techniques, one where features are derived using a model-based wavelet approach, and one where the original variables are regressed directly on the outcome.

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Point Matching

Shape Contexts¹ by Belongie, Malik, and Puzicha at Berkeley looks like a promising approach for finding point correspondences (along the lines of ICP, TPS-RPM, etc).

 

Give it a looksie whenever you get the time.

 

¹ http://www.eecs.berkeley.edu/Research/Projects/CS/vision/shape/sc_digits.html

Diffeomorphic deformation fields

Gary E. Christensen, Sarang C. Joshi, Michael I. Miller, "Volumetric Transformation of Brain Anatomy" IEEE Trans. Med. Imag. (1997) 

 

http://citeseer.ist.psu.edu/cache/papers/cs/25121/http:zSzzSzwww.icaen.uiowa.eduzSz~geczSzpaperszSzchristensen_tmi97.pdf/christensen97volumetric.pdf

 

 

Abstract

This paper presents diffeomorphic transformations of three-dimensional (3-D) anatomical image data of the macaque occipital lobe and whole brain cryosection imagery and of deep brain structures in human brains as imaged via magnetic resonance imagery. These transformations are generated in a hierarchical manner, accommodating both global and local anatomical detail. The initial low-dimensional registration is accomplished by constraining the transformation to be in a low-dimensional basis. The basis is defined by the Green’s function of the elasticity operator placed at predefined locations in the anatomy and the eigenfunctions of the elasticity operator. The high-dimensional large deformations are vector fields generated via the mismatch between the template and target-image volumes constrained to be the solution of a Navier–Stokes fluid model. As part of this procedure, the Jacobian of the transformation is tracked, insuring the generation of diffeomorphisms. It is shown that transformations constrained by quadratic regularization methods such as the Laplacian, biharmonic, and linear elasticity models, do not ensure that the transformation maintains topology and, therefore, must only be used for coarse global registration.