Literature DB >> 21827972

Image similarity and tissue overlaps as surrogates for image registration accuracy: widely used but unreliable.

Torsten Rohlfing1.   

Abstract

The accuracy of nonrigid image registrations is commonly approximated using surrogate measures such as tissue label overlap scores, image similarity, image difference, or transformation inverse consistency error. This paper provides experimental evidence that these measures, even when used in combination, cannot distinguish accurate from inaccurate registrations. To this end, we introduce a "registration" algorithm that generates highly inaccurate image transformations, yet performs extremely well in terms of the surrogate measures. Of the tested criteria, only overlap scores of localized anatomical regions reliably distinguish reasonable from inaccurate registrations, whereas image similarity and tissue overlap do not. We conclude that tissue overlap and image similarity, whether used alone or together, do not provide valid evidence for accurate registrations and should thus not be reported or accepted as such.

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Year:  2011        PMID: 21827972      PMCID: PMC3274625          DOI: 10.1109/TMI.2011.2163944

Source DB:  PubMed          Journal:  IEEE Trans Med Imaging        ISSN: 0278-0062            Impact factor:   10.048


  22 in total

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Journal:  Med Image Anal       Date:  1996-03       Impact factor: 8.545

7.  Multimodality image registration by maximization of mutual information.

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Journal:  IEEE Trans Med Imaging       Date:  1997-04       Impact factor: 10.048

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Journal:  Med Image Anal       Date:  2007-06-23       Impact factor: 8.545

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  83 in total

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Journal:  J Med Imaging (Bellingham)       Date:  2015-03-25

2.  Intensity based methods for brain MRI longitudinal registration. A study on multiple sclerosis patients.

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Review 4.  Deformable medical image registration: a survey.

Authors:  Aristeidis Sotiras; Christos Davatzikos; Nikos Paragios
Journal:  IEEE Trans Med Imaging       Date:  2013-05-31       Impact factor: 10.048

5.  A 12-step user guide for analyzing voxel-wise gray matter asymmetries in statistical parametric mapping (SPM).

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6.  Hierarchical and symmetric infant image registration by robust longitudinal-example-guided correspondence detection.

Authors:  Yao Wu; Guorong Wu; Li Wang; Brent C Munsell; Qian Wang; Weili Lin; Qianjin Feng; Wufan Chen; Dinggang Shen
Journal:  Med Phys       Date:  2015-07       Impact factor: 4.071

7.  Error estimation of deformable image registration of pulmonary CT scans using convolutional neural networks.

Authors:  Koen A J Eppenhof; Josien P W Pluim
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8.  Deformable registration of the inflated and deflated lung in cone-beam CT-guided thoracic surgery: initial investigation of a combined model- and image-driven approach.

Authors:  Ali Uneri; Sajendra Nithiananthan; Sebastian Schafer; Yoshito Otake; J Webster Stayman; Gerhard Kleinszig; Marc S Sussman; Jerry L Prince; Jeffrey H Siewerdsen
Journal:  Med Phys       Date:  2013-01       Impact factor: 4.071

9.  Next Generation Histology-Directed Imaging Mass Spectrometry Driven by Autofluorescence Microscopy.

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10.  Learning Non-rigid Deformations for Robust, Constrained Point-based Registration in Image-Guided MR-TRUS Prostate Intervention.

Authors:  John A Onofrey; Lawrence H Staib; Saradwata Sarkar; Rajesh Venkataraman; Cayce B Nawaf; Preston C Sprenkle; Xenophon Papademetris
Journal:  Med Image Anal       Date:  2017-04-12       Impact factor: 8.545

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