Accuracy Estimation for Medical Image Registration Using Regression Forests

Conference paper (2016)

Authors

Hessam Sokooti Leiden University Medical Center

G. Saygili Leiden University Medical Center

Ben Glocker Imperial College London

B.P.F. Lelieveldt Pattern Recognition and Bioinformatics - , Leiden University Medical Center

M. Staring Leiden University Medical Center

Research Group

Pattern Recognition and Bioinformatics () (TU Delft)

Image registration Uncertainty estimation Registration accuracy Regression forests

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http://resolver.tudelft.nl/uuid:9fa960e0-0c68-47b5-8a6e-6f4cfbbc16de

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Published Date

2016

Language

English

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Faculty

Electrical Engineering, Mathematics and Computer Science

Department

Intelligent Systems

Research Group

Pattern Recognition and Bioinformatics

Abstract

This paper reports a new automatic algorithm to estimate the misregistration in a quantitative manner. A random regression forest is constructed, predicting the local registration error. The forest is built using local and modality independent features related to the registration precision, the transformation model and intensity-based similarity after registration. The forest is trained and tested using manually annotated corresponding points between pairs of chest CT scans. The results show that the mean absolute error of regression is 0.72 ± 0.96 mm and the accuracy of classification in three classes (correct, poor and wrong registration) is 93.4 %, comparing favorably to a competing method. In conclusion, a method was proposed that for the first time shows the feasibility of automatic registration assessment by means of regression, and promising results were obtained.