# Anatomy Directly from Imagery: General-purpose, Scalable, and Open-source Machine Learning Approaches > **NIH NIH R01** · UTAH STATE HIGHER EDUCATION SYSTEM--UNIVERSITY OF UTAH · 2024 · $595,932 ## Abstract Project Summary The form (or shape) and function relationship of anatomical structures is a central theme in biology where abnor- mal shape changes are closely tied to pathological functions. Morphometrics has been an indispensable quan- titative tool in medical and biological sciences to study anatomical forms for more than 100 years. Recently, the increased availability of high-resolution in-vivo images of anatomy has led to the development of a new generation of morphometric approaches, called statistical shape modeling (SSM), that take advantage of modern computa- tional techniques to model anatomical shapes and their variability within populations with unprecedented detail. SSM stands to revolutionize morphometric analysis, but its widespread adoption is hindered by a number of sig- nificant challenges, including the complexity of the approaches and their increased computational requirements, relative to traditional morphometrics. Arguably, however, the most important roadblock to more widespread adop- tion is the lack of user-friendly and scalable software tools for a variety of anatomical surfaces that can be readily incorporated into biomedical research labs. The goal of this proposal is thus to address these challenges in the context of a flexible and general SSM approach termed particle-based shape modeling (PSM), which automat- ically constructs optimal statistical landmark-based shape models of ensembles of anatomical shapes without relying on any specific surface parameterization. The proposed research will provide an automated, general- purpose, and scalable computational solution for constructing shape models of general anatomy. In Aim 1, we will build computational and machine learning algorithms to model anatomies with complex surface topologies (e.g., surface openings and shared boundaries) and highly variable anatomical populations. In Aim 2, we will introduce an end-to-end machine learning approach to extract statistical shape representation directly from im- ages, requiring no parameter tuning, image pre-processing, or user assistance. In Aim 3, we will provide intuitive graphical user interfaces and visualization tools to incorporate user-defined modeling preferences and promote the visual interpretation of shape models. We will also make use of recent advances in cloud computing to enable researchers with limited computational resources and/or large cohorts to build and execute custom SSM work- flows using remote scalable computational resources. Algorithmic developments will be thoroughly evaluated and validated using existing, fully funded, large-scale, and constantly growing databases of CT and MRI images lo- cated on-site. Furthermore, we will develop and disseminate standard workflows and domain-specific use cases for complex anatomies to promote reproducibility. Efforts to develop the proposed technology are aligned with the mission of the National Institute of General Medical Sciences (NIGMS), and its third strategic goal: ... ## Key facts - **NIH application ID:** 10844342 - **Project number:** 5R01AR076120-04 - **Recipient organization:** UTAH STATE HIGHER EDUCATION SYSTEM--UNIVERSITY OF UTAH - **Principal Investigator:** Shireen Youssef Elhabian - **Activity code:** R01 (R01, R21, SBIR, etc.) - **Funding institute:** NIH - **Fiscal year:** 2024 - **Award amount:** $595,932 - **Award type:** 5 - **Project period:** 2019-07-01 → 2026-05-31 ## Primary source NIH RePORTER: https://reporter.nih.gov/project-details/10844342 ## Citation > US National Institutes of Health, RePORTER application 10844342, Anatomy Directly from Imagery: General-purpose, Scalable, and Open-source Machine Learning Approaches (5R01AR076120-04). Retrieved via AI Analytics 2026-05-23 from https://api.ai-analytics.org/grant/nih/10844342. Licensed CC0. --- *[NIH grants dataset](/datasets/nih-grants) · CC0 1.0*