# Improving Virtual Gross Anatomy: Enhancing the Information Content of Cadaveric CT Scans

> **NIH NIH F31** · STATE UNIVERSITY OF NEW YORK AT BUFFALO · 2022 · $31,521

## Abstract

Project Summary
The long-term goal of this research is to establish a pipeline for automated image processing that enhances
cadaveric non-contrast enhanced (NCE) CT data and extracts meaningful models and metrics to improve
anatomy research and education. The objective is to develop the necessary toolset for this image processing,
and feature extraction. The central hypothesis is that it is possible to enrich the information content of biomedical
imaging data, particularly that of cadaveric NCE CT imaging, for use in gross anatomy education and research.
The rationale behind this project is that cadaveric dissection, while an important part of anatomy education, is
limited due to sample size, infrastructure, cost, and time. Biomedical imaging can preserve specimens for
posterity and be used to supplement this material by providing statistical and quantitative information from
anatomical structures. This research will attempt to establish a working pipeline for efficient information extraction
through the following specific aims: (1) Improving inter-observer anatomical agreement in cadaveric CT
scans; (2) Develop an approach to automatically segment anatomical structures from non-contrast
enhanced CT images; and (3) Establish normal variation of anatomical structures and its relationship to
pathologies. This project is innovative because it applies artificial intelligence to efficiently extract anatomical
information from cadaveric NCE CT imaging, which has only been performed with traditional registration-
dependent methods that often fail and are domain specific, acting on a single organ at a time. In addition, this
project works with multi-species data to enhance human image data.
 This project is significant because it will allow students to understand anatomical variation better by both
expanding student exposure to more samples, while also extracting useful representations and analytics from
these samples for education and research. The expected outcome of this project is a toolset that is capable of
enhancing anatomy education and research by increasing soft-tissue contrast, automatically segmenting the
kidneys, liver, mandible, and intraosseus sites of the cranial nerves, and performing statistical analysis on these
organs, including but not limited to statistical shape modelling and shape analysis. This will have a positive
impact on anatomical education and student retention because it will provide students with a broader range of
sample variability information which will decrease pervading biases in medical training that result from small,
limited sample sizes, and improve medical training.

## Key facts

- **NIH application ID:** 10377915
- **Project number:** 5F31EB030904-02
- **Recipient organization:** STATE UNIVERSITY OF NEW YORK AT BUFFALO
- **Principal Investigator:** Steven Lewis
- **Activity code:** F31 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2022
- **Award amount:** $31,521
- **Award type:** 5
- **Project period:** 2021-04-01 → 2023-03-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10377915

## Citation

> US National Institutes of Health, RePORTER application 10377915, Improving Virtual Gross Anatomy: Enhancing the Information Content of Cadaveric CT Scans (5F31EB030904-02). Retrieved via AI Analytics 2026-05-24 from https://api.ai-analytics.org/grant/nih/10377915. Licensed CC0.

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