Accuracy Estimation for Medical Image Registration Using Regression Forests

Hessam Sokooti, Gorkem Saygili, Ben Glocker, Boudewijn Lelieveldt, Marius Staring

Research output: Chapter in Book/Conference proceedings/Edited volumeConference contributionScientificpeer-review

15 Citations (Scopus)


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.
Original languageEnglish
Title of host publicationMedical Image Computing and Computer-Assisted Intervention MICCAI 2016
Subtitle of host publication19th International Conference, Proceedings part 3
EditorsS. Ourselin, L. Joskowicz, M. Sabuncu , G. Unal, W. Wells
Place of PublicationCham
Number of pages9
ISBN (Electronic)978-3-319-46726-9
ISBN (Print)978-3-319-46725-2
Publication statusPublished - 2016
EventMICCAI 2016 - Medical Image Computing and Computer-Assisted Intervention: 19th International Conference - Athens, Greece
Duration: 16 Oct 201621 Oct 2016

Publication series

NameLecture Notes in Computer Science
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


ConferenceMICCAI 2016 - Medical Image Computing and Computer-Assisted Intervention
Abbreviated titleMICCAI 2016


  • Image registration
  • Registration accuracy
  • Uncertainty estimation
  • Regression forests

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