The anatomical structure of an organism can provide important clues about its adaptation to its environment, its evolutionary history and its systematic relationships. The morphometric (quantitative) study of the organism?s shape and function is typically conducted at natural history museums, by measuring the specimen directly. High-resolution 3D imaging techniques (e.g., micro computed tomography) provide us to capture the internal and external anatomy of biological specimens in unprecedented detail. Likewise, online virtual "collections" (e.g., MorphoSource) provide a convenient access to these datasets, without requiring scientists to travel to different museums to collect their data. However, the complex nature of these 3D datasets makes them challenging to analyze for most researchers. In this project, we propose to develop an open-source software to retrieve and visualize 3D biological specimens from online museums. We will also develop tools for the scientists to measure and analyze the anatomy more easily.
Geometric Morphometrics Methods (GMM) are a set of quantitative analytical techniques that rely on landmarks to capture organismal shape and form. This project proposes to develop a GMM module for open-source 3D-Slicer (Slicer) visualization software as an integrated toolkit to address challenges researchers face working with 3D anatomical data. In addition to manual annotation of landmarks, the toolkit will provide two distinct (user-guided and automated) types of semi-landmarking methods to enable general purpose dense spatial sampling for different study designs. Annotated landmark data can be analyzed in the GMM module using the Generalized Procrustes Analysis (GPA). Shape variation associated with GPA can be decomposed as principal warps and can be visualized in full 3D. Toolkit will also provide a data aggregator (SpecMart) that will query online 3D specimen repository MorphoSource by taxonomy (and other tokens), retrieve and visualize data within Slicer. We will train students in theory and application of 3D image segmentation, analysis and visualization as it applies to nonmedical volumetric imaging datasets to facilitate reproducible and high-throughput analysis of such datasets. The toolkit will be developed as an add-on package to Slicer using open-source toolchains and software development platforms (such as git). The stable versions of the module will be distributed through Extension Manager of Slicer (and its online portal) and documentation will be hosted on their site. The open-source nature of this development and use of the Slicer community's existing infrastructure that has been in existence more than 15 years to distribute and support the toolkit will mean that the software will be widely available for many years to come and can be supported (and potentially expanded) by the user community.
This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.
