Using incomplete and imprecise localization data on images to improve estimates of detection accuracy

Richard G. Swensson; Glenn S. Maitz; Jill L. King; David Gur

doi:10.1117/12.349665

24 May 1999 Using incomplete and imprecise localization data on images to improve estimates of detection accuracy

Richard G. Swensson, Glenn S. Maitz, Jill L. King, David Gur

Proceedings Volume 3663, Medical Imaging 1999: Image Perception and Performance; (1999) https://doi.org/10.1117/12.349665
Event: Medical Imaging '99, 1999, San Diego, CA, United States

Abstract

We tested new analytic procedures for combining an observer's image-ratings of lesion-likelihood with localization reports that are incomplete (unavailable on images rated as 'normal') and/or imprecise (possibly scored as 'correct' by chance), and for fitting a constrained ROC formulation to the rating data alone. Eight radiologist readers in a previous study had rated the likelihood of nodular lesions on each of 250 chest-film cases (39 with subtle nodules, 36 with 'typical' nodules and 175 normal cases) that were presented in two display modes (original films or on video workstation). Ratings in the four positive categories (2 to 5) were accompanied by reports that grossly localized the suspected nodules into one of 7 film- regions (upper, middle or lower portions of left or right lung field, or retrocardiac), but there was no localization for the cases rated as 'normal' (category 1). In each of 29 sets of data, we estimated the area below the ROC curve (A_z) and its standard error using three different fits: (1) the usual ROC formulation, (2) the constrained ROC formulation and (3) the new procedure that included incomplete and imprecise localization data (I&I). Estimates of A_z from the usual and constrained ROC fits were quite similar unless the standard ROC exhibited an upward 'hook,' but standard errors of A_z were always the same or smaller for the constrained ROC fit. The I&I fit that included localization data often estimated A_z to be either larger or smaller than the usual or constrained ROC fits that considered only the rating data, but its A_z had substantially smaller standard errors in 28 of the 29 sets of observer data.

Citation Download Citation

Richard G. Swensson, Glenn S. Maitz, Jill L. King, and David Gur "Using incomplete and imprecise localization data on images to improve estimates of detection accuracy", Proc. SPIE 3663, Medical Imaging 1999: Image Perception and Performance, (24 May 1999); https://doi.org/10.1117/12.349665

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