# An interactive deep-learning method to semi-automatically segment abdominal organs to support stereotactic MR guided online adaptive radiotherapy (SMART) for abdominal cancers

> **NIH NIH R03** · WASHINGTON UNIVERSITY · 2020 · $60,767

## Abstract

Abstract
 Stereotactic MRI-guided online adaptive radiotherapy (SMART) is an effective treatment for
the pancreas and other upper abdominal cancers. SMART allows precise delivery of escalated
prescription dose to the abdominal tumor targets while avoiding the complications of radiation
toxicity to the mobile gastrointestinal (GI) organs surrounding the tumor target. In the clinical
workflow of SMART, manual segmentation of the GI orangs at risk (OARs) is one of the most
important but also the most labor-intensive steps. Manual segmentation takes 10 minutes on
average but ranges from 5 to 22 minutes. The slow and costly manual segmentation step directly
decreases the accessibility and affordability of online SMART and indirectly reduces the
effectiveness of SMART due to intra-fractional body and organ movement of the patients. In this
study, we will develop a deep-learning based interactive and semi-automatic procedure to
accurately and quickly segment the GI OARs to make SMART more efficient and affordable.

## Key facts

- **NIH application ID:** 10017990
- **Project number:** 5R03EB028427-02
- **Recipient organization:** WASHINGTON UNIVERSITY
- **Principal Investigator:** Deshan Yang
- **Activity code:** R03 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2020
- **Award amount:** $60,767
- **Award type:** 5
- **Project period:** 2019-09-16 → 2021-12-22

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10017990

## Citation

> US National Institutes of Health, RePORTER application 10017990, An interactive deep-learning method to semi-automatically segment abdominal organs to support stereotactic MR guided online adaptive radiotherapy (SMART) for abdominal cancers (5R03EB028427-02). Retrieved via AI Analytics 2026-05-23 from https://api.ai-analytics.org/grant/nih/10017990. Licensed CC0.

---

*[NIH grants dataset](/datasets/nih-grants) · CC0 1.0*