Uploaded April 2021 | Updated September 2026, 2 weeks ago
Guest Speaker: Lorin Crawford, RGSS Assistant Professor of Biostatistics, Brown University; and Senior Researcher at Microsoft Research New England
Chaired by: Brielin Brown, DSI Postdoctoral Research Scientist
Statistical Frameworks for Mapping 3D Shape Variation onto Genotypic and Phenotypic Variation
Abstract: The recent curation of large-scale databases with 3D surface scans of shapes has motivated the development of tools that better detect global-patterns in morphological variation. Studies which focus on identifying differences between shapes have been limited to simple pairwise comparisons and rely on pre-specified landmarks (that are often known). In this talk, we present SINATRA: a statistical pipeline for analyzing collections of shapes without requiring any correspondences. Our method takes in two classes of shapes and highlights the physical features that best describe the variation between them.
The SINATRA pipeline implements four key steps. First, SINATRA summarizes the geometry of 3D shapes (represented as triangular meshes) by a collection of vectors (or curves) that encode changes in their topology. Second, a nonlinear Gaussian process model, with the topological summaries as input, classifies the shapes. Third, an effect size analog and corresponding association metric is computed for each topological feature used in the classification model. These quantities provide evidence that a given topological feature is associated with a particular class. Fourth, the pipeline iteratively maps the topological features back onto the original shapes (in rank order according to their association measures) via a reconstruction algorithm. This highlights the physical (spatial) locations that best explain the variation between the two groups.
We use a rigorous simulation framework to assess our approach, which themselves are a novel contribution to 3D image analysis. Lastly, as a case study, we use SINATRA to analyze mandibular molars from four different suborders of primates and demonstrate its ability recover known morphometric variation across phylogenies.
Bio: Lorin Crawford is a Senior Researcher at Microsoft Research New England. He also holds a faculty position as the RGSS Assistant Professor of Biostatistics at Brown University with an affiliation in the Center for Computational Molecular Biology. His scientific research interests involve the development of novel and efficient computational methodologies to address complex problems in statistical genetics, cancer pharmacology, and radiomics (e.g., cancer imaging). The central aim of Dr. Crawford’s research program is to build machine learning algorithms and statistical tools that aid in the understanding of how nonlinear interactions between genetic features affect the architecture of complex traits and contribute to disease etiology. An overarching theme of the research done in the Crawford Lab group is to take modern computational approaches and develop theory that enable their interpretations to be related back to classical genomic principles.
Before joining both MSR and Brown, Dr. Crawford received his PhD from the Department of Statistical Science at Duke University where he was formerly co-advised by Drs. Sayan Mukherjee and Kris C. Wood. He also received his Bachelors of Science degree in Mathematics from Clark Atlanta University.
Guest Speaker: Lorin Crawford, RGSS Assistant Professor of Biostatistics, Brown University; and Senior Researcher at Microsoft Research New England
Chaired by: Brielin Brown, DSI Postdoctoral Research Scientist
Statistical Frameworks for Mapping 3D Shape Variation onto Genotypic and Phenotypic Variation
Abstract: The recent curation of large-scale databases with 3D surface scans of shapes has motivated the development of tools that better detect global-patterns in morphological variation. Studies which focus on identifying differences between shapes have been limited to simple pairwise comparisons and rely on pre-specified landmarks (that are often known). In this talk, we present SINATRA: a statistical pipeline for analyzing collections of shapes without requiring any correspondences. Our method takes in two classes of shapes and highlights the physical features that best describe the variation between them.
The SINATRA pipeline implements four key steps. First, SINATRA summarizes the geometry of 3D shapes (represented as triangular meshes) by a collection of vectors (or curves) that encode changes in their topology. Second, a nonlinear Gaussian process model, with the topological summaries as input, classifies the shapes. Third, an effect size analog and corresponding association metric is computed for each topological feature used in the classification model. These quantities provide evidence that a given topological feature is associated with a particular class. Fourth, the pipeline iteratively maps the topological features back onto the original shapes (in rank order according to their association measures) via a reconstruction algorithm. This highlights the physical (spatial) locations that best explain the variation between the two groups.
We use a rigorous simulation framework to assess our approach, which themselves are a novel contribution to 3D image analysis. Lastly, as a case study, we use SINATRA to analyze mandibular molars from four different suborders of primates and demonstrate its ability recover known morphometric variation across phylogenies.
Bio: Lorin Crawford is a Senior Researcher at Microsoft Research New England. He also holds a faculty position as the RGSS Assistant Professor of Biostatistics at Brown University with an affiliation in the Center for Computational Molecular Biology. His scientific research interests involve the development of novel and efficient computational methodologies to address complex problems in statistical genetics, cancer pharmacology, and radiomics (e.g., cancer imaging). The central aim of Dr. Crawford’s research program is to build machine learning algorithms and statistical tools that aid in the understanding of how nonlinear interactions between genetic features affect the architecture of complex traits and contribute to disease etiology. An overarching theme of the research done in the Crawford Lab group is to take modern computational approaches and develop theory that enable their interpretations to be related back to classical genomic principles.
Before joining both MSR and Brown, Dr. Crawford received his PhD from the Department of Statistical Science at Duke University where he was formerly co-advised by Drs. Sayan Mukherjee and Kris C. Wood. He also received his Bachelors of Science degree in Mathematics from Clark Atlanta University.
