Literature DB >> 26866048

Full receiver operating characteristic curve estimation using two alternative forced choice studies.

Francesc Massanes1, Jovan G Brankov1.   

Abstract

Task-based medical image quality is typically measured by the degree to which a human observer can perform a diagnostic task in a psychophysical human observer study. During a typical study, an observer is asked to provide a numerical score quantifying his confidence as to whether an image contains a diagnostic marker or not. Such scores are then used to measure the observers' diagnostic accuracy, summarized by the receiver operating characteristic (ROC) curve and the area under ROC curve. These types of human studies are difficult to arrange, costly, and time consuming. In addition, human observers involved in this type of study should be experts on the image genre to avoid inconsistent scoring through the lengthy study. In two-alternative forced choice (2AFC) studies, known to be faster, two images are compared simultaneously and a single indicator is given. Unfortunately, the 2AFC approach cannot lead to a full ROC curve or a set of image scores. The aim of this work is to propose a methodology in which multiple rounds of the 2AFC studies are used to re-estimate an image confidence score (a.k.a. rating, ranking) and generate the full ROC curve. In the proposed approach, we treat image confidence score as an unknown rating that needs to be estimated and 2AFC as a two-player match game. To achieve this, we use the ELO rating system, which is used for calculating the relative skill levels of players in competitor-versus-competitor games such as chess. The proposed methodology is not limited to ELO, and other rating methods such as TrueSkill™, Chessmetrics, or Glicko can be also used. The presented results, using simulated data, indicate that a full ROC curve can be recovered using several rounds of 2AFC studies and that the best pairing strategy starts with the first round of pairing abnormal versus normal images (as in the classical 2AFC approach) followed by a number of rounds using random pairing. In addition, the proposed method was tested in a pilot human observer study. These pilot results indicate that three to five rounds of 2AFC studies require less human observer time than a full scoring study and that the re-estimated ROC curves and associated area under ROC curve values have high statistical agreement with the full scoring study.

Entities:  

Keywords:  computer tomography; image evaluation; image quality; reader studies

Year:  2016        PMID: 26866048      PMCID: PMC4742614          DOI: 10.1117/1.JMI.3.1.011010

Source DB:  PubMed          Journal:  J Med Imaging (Bellingham)        ISSN: 2329-4302


  14 in total

1.  The partial area under the summary ROC curve.

Authors:  S D Walter
Journal:  Stat Med       Date:  2005-07-15       Impact factor: 2.373

2.  Practical issues of experimental ROC analysis. Selection of controls.

Authors:  D Gur; J L King; H E Rockette; C A Britton; F L Thaete; R J Hoy
Journal:  Invest Radiol       Date:  1990-05       Impact factor: 6.016

3.  Generalization Evaluation of Machine Learning Numerical Observers for Image Quality Assessment.

Authors:  Mahdi M Kalayeh; Thibault Marin; Jovan G Brankov
Journal:  IEEE Trans Nucl Sci       Date:  2013-06       Impact factor: 1.679

4.  The meaning and use of the area under a receiver operating characteristic (ROC) curve.

Authors:  J A Hanley; B J McNeil
Journal:  Radiology       Date:  1982-04       Impact factor: 11.105

5.  Efficiency of the human observer detecting random signals in random backgrounds.

Authors:  Subok Park; Eric Clarkson; Matthew A Kupinski; Harrison H Barrett
Journal:  J Opt Soc Am A Opt Image Sci Vis       Date:  2005-01       Impact factor: 2.129

6.  Machine Learning in Medical Imaging.

Authors:  Miles N Wernick; Yongyi Yang; Jovan G Brankov; Grigori Yourganov; Stephen C Strother
Journal:  IEEE Signal Process Mag       Date:  2010-07       Impact factor: 12.551

7.  Comparison of receiver operating characteristic and forced choice observer performance measurement methods.

Authors:  A E Burgess
Journal:  Med Phys       Date:  1995-05       Impact factor: 4.071

8.  Testing signal-detection models of yes/no and two-alternative forced-choice recognition memory.

Authors:  Yoonhee Jang; John T Wixted; David E Huber
Journal:  J Exp Psychol Gen       Date:  2009-05

9.  Learning a channelized observer for image quality assessment.

Authors:  Jovan G Brankov; Yongyi Yang; Liyang Wei; Issam El Naqa; Miles N Wernick
Journal:  IEEE Trans Med Imaging       Date:  2009-02-10       Impact factor: 10.048

10.  Numerical Surrogates for Human Observers in Myocardial Motion Evaluation From SPECT Images.

Authors:  Thibault Marin; Mahdi M Kalayeh; Felipe M Parages; Jovan G Brankov
Journal:  IEEE Trans Med Imaging       Date:  2013-08-22       Impact factor: 10.048

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