This application is submitted in response to NOT-OD-20-073 as an administrative supplement to the parent award R01AR076120 titled: Anatomy Directly from Imagery: General-purpose, Scalable, and Open-source Machine Learning Approaches. The form (or shape) of anatomies is the clinical language that describes abnormal mor- phologies tied to pathologic functions. Quantifying such subtle morphological shape changes requires parsing the anatomy into a quantitative description that is consistent across the population in question. For more than 100 years, morphometrics has been an indispensable quantitative tool in medical and biological sciences to study anatomical forms. But its representation capacity is limited to linear distances, angles, and areas. Sta- tistical shape modeling (SSM) is the computational extension of classical morphometric techniques to analyze more detailed representations of complex anatomy and their variability within populations The parent award ad- dresses existing roadblocks for the widespread adoption of SSM computational tools in the context of a ?exible and general SSM approach termed particle-based shape modeling (PSM) and its associated suite of open-source software tools, ShapeWorks. ShapeWorks enables learning population-level shape representation via automatic dense placement of homologous landmarks on image segmentations of general anatomy with arbitrary topology. The utility of ShapeWorks has been demonstrated in a range of biomedical applications. ShapeWorks has the potential to transform the way researchers approach studies of anatomical forms, but its widespread applicability and impact to medicine and biology are hindered by computational barriers that most existing shape modeling packages face. The goal of this supplement award is to provide supplemental support for Aim 3 of the parent award to leverage best practices in software development and advances in cloud computing to enable researchers with limited computational resources and/or large-scale cohorts to build and execute custom SSM work?ows us- ing remote scalable computational resources. To achieve this goal, we have developed a plan to enhance the design, implementation, and cloud-readiness of ShapeWorks and augmented our scienti?c team to add senior, experienced software engineers/developers who have extensive experience in professional programming, code refactoring, and scienti?c computing. This award will provide our team with the support necessary to (Aim 1) de- sign ShapeWorks as a collection of modular and reusable services, (Aim 2) decouple ShapeWorks services from explicitly encoded data sources, and (Aim 3) refactor ShapeWorks to scale ef?ciently on the cloud. All software development will be performed in adherence to software engineering practices and design principles, including coding style, documentation, and version control. The proposed efforts will be released as open-source software in a manner consistent with the principles of reproducible research and the practices of open science. Our long- term goal is to make ShapeWorks a standard tool for shape analyses in medicine, and the work proposed herein in addition to the parent award will establish the groundwork for achieving this goal.
ShapeWorks is a free, open-source software tool that uses a ?exible method for automated construction of sta- tistical landmark-based shape models of ensembles of anatomical shapes. The impact and scienti?c value of ShapeWorks have been recognized in a range of applications, including psychology, biological phenotyping, car- diology, and orthopedics. If funded, this supplement will provide support to revise, refactor, and redeploy Shape- Works to take advantage of new cloud computing paradigms, to be robust, sustainable, scalable, and accessible to a broader community, and to address the growing need for shape modeling tools to handle large collections of clinical data and to obtain suf?cient statistical power for large shape studies.