A fast and scalable method for quality assurance of deformable image registration on lung CT scans using convolutional neural networks

Shaikat M. Galib, Hyoung K. Lee, Christopher L. Guy, Matthew J. Riblett, Geoffrey D. Hugo

Research output: Contribution to journalArticle

Abstract

Purpose: To develop and evaluate a method to automatically identify and quantify deformable image registration (DIR) errors between lung computed tomography (CT) scans for quality assurance (QA) purposes. Methods: We propose a deep learning method to flag registration errors. The method involves preparation of a dataset for machine learning model training and testing, design of a three-dimensional (3D) convolutional neural network architecture that classifies registrations into good or poor classes, and evaluation of a metric called registration error index (REI) which provides a quantitative measure of registration error. Results: Our study shows that, despite having limited number of training images available (10 CT scan pairs for training and 17 CT scan pairs for testing), the method achieves 0.882 AUC-ROC on the test dataset. Furthermore, the combined standard uncertainty of the estimated REI by our model lies within ± 0.11 (± 11% of true REI value), with a confidence level of approximately 68%. Conclusions: We have developed and evaluated our method using original clinical registrations without generating any synthetic/simulated data. Moreover, test data were acquired from a different environment than that of training data, so that the method was validated robustly. The results of this study showed that our algorithm performs reasonably well in challenging scenarios.

Original languageEnglish
Pages (from-to)99-109
Number of pages11
JournalMedical physics
Volume47
Issue number1
DOIs
StatePublished - Jan 1 2020

Keywords

  • deep learning
  • image registration
  • neural network
  • quality assurance

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