Center for Bioimage Informatics at Carnegie Mellon University: Research Projects

Research Projects
	Deformation-Based Nuclear Morphometry: Capturing nuclear shape variation in HeLa cells Led by: Gustavo Rohde, Kris Noel Dahl and Robert F. Murphy Information available at: http://www.andrew.cmu.edu/user/gustavor/research.html The empirical characterization of nuclear shape distributions is an important unsolved problem with many applications in biology and medicine. Numerous genetic diseases and cancers have alterations in nuclear morphology, and methods for characterization of morphology could aid in both diagnoses and fundamental understanding of these disorders. Automated approaches have been used to measure features related to the size and shape of the cell nucleus, and statistical analysis of these features has often been performed assuming an underlying Euclidean (linear) vector space. We discuss the difficulties associated with the analysis of nuclear shape in light of the fact that shape spaces are nonlinear, and demonstrate methods for characterizing nuclear shapes and shape distributions based on spatial transformations that map one nucleus to another. By combining large deformation metric mapping with multidimensional scaling we offer a flexible approach for elucidating the intrinsic nonlinear degrees of freedom of a distribution of nuclear shapes. More specifically, we demonstrate approaches for nuclear shape interpolation and computation of mean nuclear shape. We also provide a method for estimating the number of free parameters that contribute to shape as well as an approach for visualizing most representative shape variations within a distribution of nuclei. The proposed methodology can be completely automated, is independent of the dimensionality of the images, and can handle complex shapes. Results obtained by analyzing two sets of images of HeLa cells are shown. In addition to identifying the modes of variation in normal HeLa nuclei, the effects of lamin A/C on nuclear morphology are quantitatively described.

	Golgi-Body Segmentation Led by: Adam Linstedt and Jelena Kovacevic Information available at: http://www.andrew.cmu.edu/user/jelenak/Repository/08_SrinivasaFGLK/08_SrinivasaFGLK.html We present a novel active mask framework for the segmentation of fluorescence microscope images of cells, and in particular, for the segmentation of the Golgi body as well as cell-volume computation. We demonstrate that the algorithm is able to efficiently segment a stack of images and successfully assign multiple pieces of the Golgi body in a 2D image to the cell to which they belong. Further, we demonstrate that our algorithm is more accurate than manual segmentation of these images.

	Identification of Germ Layer Components in Teratomas Derived from Embryonic Stem Cells Led by: John Ozolek, Carlos Castro, Gustavo Rohde and Jelena Kovacevic Information available at: http://www.andrew.cmu.edu/user/jelenak/WWW/Repository/08_ISBI_Classification.pdf We propose a system for identification of germ layer components in teratomas derived from human and nonhuman primate embryonic stem cells. Tissue regeneration and repair, drug testing and discovery, the cure of genetic and developmental syndromes all may rest on the understanding of the biology and behavior of embryonic stem (ES) cells. Within the field of stem cell biology, an ES cell is not considered an ES cell until it can produce a teratoma tumor (the ``gold'' standard test); a seemingly disorganized mass of tissue derived from all three embryonic germ layers; ectoderm, mesoderm, and endoderm. Identification and quantification of tissue types within teratomas derived from ES cells may expand our knowledge of abnormal and normal developmental programming and the response of ES cells to genetic manipulation and/or toxic exposures. In addition, because of the tissue complexity, identifying and quantifying the tissue is tedious and time consuming, but in turn the teratomas provides an excellent biological platform to test robust image analysis algorithms. We use a multiresolution (MR) classification system with texture features, as well as develop novel nuclear texture features to recognize germ layer components. With redundant MR transform, we achieve a classification accuracy of approximately 88%.

	Image Analysis for High-Throughput Drosophila Embryo RNAi Screens Led by: Stefan Zappe, Jonathan Minden and Jelena Kovacevic Information available at: http://www.andrew.cmu.edu/user/jelenak/Repository/07_04_ISBI_KelloggCGCZMK.pdf We work towards an image analysis toolbox for high-throughput Drosophila embryo RNAi screens. In this first step, the goal is to tag the embryo as normal, developmentally delayed or abnormal based on the ventral furrow formation. We break the problem into two parts: in the first, we detect the developmental stage based on the progress of the ventral furrow formation, and in the second, we tag the embryo as normal/developmentally delayed/abnormal based on the stage detected and the elapsed time. The crux of the algorithm is the multiresolution classifier, and we show that, by classifying in multiresolution spaces, we obtain better results than by classifying the embryo image alone. The final 2D accuracy obtained was 93.17%, while by using 3D information, it increased to 98.35%.

	Location Proteomics Led by: Robert F. Murphy Information available at: http://murphylab.web.cmu.edu/ The primary focus of current work in the lab is on automated interpretation of fluorescence microscope images.