# Develop a large-scale library of comprehensive deformable image registration (DIR) benchmark datasets and an integrated framework for quantifying accuracy of patient-specific DIR results

> **NIH NIH R01** · WASHINGTON UNIVERSITY · 2021 · $105,767

## Abstract

Summary
 Deformable image registration (DIR) between different image sets acquired from the same patient is
a key enabling technology for many important diagnostic and therapeutic tasks, e.g. tumor diagnosis,
evaluation of tumor response to treatment, and image-guided surgery. DIR algorithms compute tissue
deformation by maximizing intensity and/or structural similarity between moving and target images, and
regularity of deformation. DIR accuracy, which is the voxel-level positional correspondence between the
two images, is not guaranteed, often inadequate, unpredictable and patient specific. DIR accuracy is
largely dependent on anatomical site, image modality and quality, algorithm designs and
implementations, operator skills and workflow selections. Inaccurate DIRs can have significant
deleterious impact clinical decisions, treatment quality and patient safety. Lack of confidence in current
registration tools has significantly limited the broader use of DIR in automating clinical decision-making
tasks and improving diagnostic and therapeutic outcomes.
 We posit that lack of accurate or robust performance arises from the fact that current DIR algorithms
are based upon overly simplistic models of tissue deformation and failure to accommodate the reality of
CT image quality. Currently, no method exists for quantitatively and automatically evaluating patient-
specific DIR accuracy. We are therefore motivated to conduct two studies:
1) Build a large and comprehensive library of DIR benchmark datasets to support DIR algorithm
validation in challenging settings. Each new DIR benchmark dataset will consist of automatically and
 precisely detected landmark pairs, small blood vessel section pairs, and segmentation of organs and
 large blood vessels. Currently no such DIR benchmark dataset exist. These datasets will spur
 development of new and advanced DIR algorithms able to support complex, patient-specific tissue
 deformation. These datasets will be immensely valuable for applications beyond DIR such as
 semantic segmentation and vessels extraction, etc.
2) Develop integrated methods for quantitative verification of patient-specific DIRs. The automatic DIR
 verification procedure will use multiple novel deep-learning models for automatic organ
 segmentation, vessel bifurcation detection and direct prediction of 3D vector field of TREs (target
 registration error). These to-be-developed deep-learning-based image processing procedures are
 robust with respect to image noise and intensity variations, and will naturally support many
 anatomical sites. This DIR verification procedure will provide quality assurance for patient-specific
 DIRs for supporting clinical applications.

## Key facts

- **NIH application ID:** 10121241
- **Project number:** 1R01EB029431-01A1
- **Recipient organization:** WASHINGTON UNIVERSITY
- **Principal Investigator:** Deshan Yang
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2021
- **Award amount:** $105,767
- **Award type:** 1
- **Project period:** 2021-08-02 → 2021-12-22

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10121241

## Citation

> US National Institutes of Health, RePORTER application 10121241, Develop a large-scale library of comprehensive deformable image registration (DIR) benchmark datasets and an integrated framework for quantifying accuracy of patient-specific DIR results (1R01EB029431-01A1). Retrieved via AI Analytics 2026-05-23 from https://api.ai-analytics.org/grant/nih/10121241. Licensed CC0.

---

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