# Machine Learning Inspired Physical Models in Organs

> **NIH NIH F31** · RICE UNIVERSITY · 2021 · $46,036

## Abstract

PROJECT SUMMARY
 The vascular system plays a crucial role in diagnostics, treatment, and surgical planning in a wide array
of diseases. Historically, practitioners locate vessel manually on each image of a CT scan. This is a tedious
process that can vary highly depending on the individual's experience and ability. Recently, there has been
motivation to automate this process to save time and increase accuracy. This process, vessel segmentation,
is challenging because of the small size of the vessel structure and the varying contrast and noise in medical
images. Current image processing techniques have not been successful in resolving the full vascular systems in
humans because of these challenges. However, a novel neural network algorithm has shown potential to reduce
training times and increase accuracy per degree of freedom in medical imaging segmentation. Applying this
algorithm in the liver vessel segmentation, and eventually other organs' vascular system segmentation shows
great promise. In addition to achieving successful vessel segmentation of the full vascular system, there is
motivation to create a model that simulates blood flow and mass transportation in the vascular system. This is
accomplished by using coupled multidimensional computational models for the flow and transport within the blood
vessels. The combination of these two aims will give a complete overview of the location and function
of a patient's circulatory system. This research will be completed by the joint effort of the Computational
and Applied Mathematics Department at Rice University and the Department of Imaging Physics, Division of
Diagnostic Imaging at The University of Texas MD Anderson Cancer Center. The collaborative nature of this
project allows mathematicians to work with physicians who are experienced in the diagnosis and treatment of
many diseases. Leveraging everyone's strengths and background will allow for a successful development and
implementation of this project.

## Key facts

- **NIH application ID:** 10315919
- **Project number:** 1F31HL160164-01
- **Recipient organization:** RICE UNIVERSITY
- **Principal Investigator:** Bilyana Tzolova
- **Activity code:** F31 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2021
- **Award amount:** $46,036
- **Award type:** 1
- **Project period:** 2021-09-01 → 2024-08-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10315919

## Citation

> US National Institutes of Health, RePORTER application 10315919, Machine Learning Inspired Physical Models in Organs (1F31HL160164-01). Retrieved via AI Analytics 2026-05-23 from https://api.ai-analytics.org/grant/nih/10315919. Licensed CC0.

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