title: Impact of lesion segmentation metrics on computer-aided diagnosis/detection in breast computed tomography.
abstract: Evaluation of segmentation algorithms usually involves comparisons of segmentations to gold-standard delineations without regard to the ultimate medical decision-making task. We compare two segmentation evaluations methods-a Dice similarity coefficient (DSC) evaluation and a diagnostic classification task-based evaluation method using lesions from breast computed tomography. In our investigation, we use results from two previously developed lesion-segmentation algorithms [a global active contour model (GAC) and a global with local aspects active contour model]. Although similar DSC values were obtained (0.80 versus 0.77), we show that the global + local active contour (GLAC) model, as compared with the GAC model, is able to yield significantly improved classification performance in terms of area under the receivers operating characteristic (ROC) curve in the task of distinguishing malignant from benign lesions. [Area under the [Formula: see text] compared to 0.63, [Formula: see text]]. This is mainly because the GLAC model yields better detailed information required in the calculation of morphological features. Based on our findings, we conclude that the DSC metric alone is not sufficient for evaluating segmentation lesions in computer-aided diagnosis tasks.
authors.affiliation: University of Chicago , Department of Radiology, 5841 S. Maryland Avenue, Chicago 60637, Illinois, United States.
authors.forename: Hsien-Chi
authors.initials: HC
authors.lastname: Kuo
authors.affiliation: University of Chicago , Department of Radiology, 5841 S. Maryland Avenue, Chicago 60637, Illinois, United States.
authors.forename: Maryellen L
authors.initials: ML
authors.lastname: Giger
authors.affiliation: University of Chicago , Department of Radiology, 5841 S. Maryland Avenue, Chicago 60637, Illinois, United States.
authors.forename: Ingrid
authors.initials: I
authors.lastname: Reiser
authors.affiliation: University of Chicago , Department of Radiology, 5841 S. Maryland Avenue, Chicago 60637, Illinois, United States.
authors.forename: Karen
authors.initials: K
authors.lastname: Drukker
authors.affiliation: University of California at Davis , Department of Radiology, 4860 Y Street, Suite 3100, Sacramento 95817, California, United States.
authors.forename: John M
authors.initials: JM
authors.lastname: Boone
authors.affiliation: University of California at Davis , Department of Radiology, 4860 Y Street, Suite 3100, Sacramento 95817, California, United States.
authors.forename: Karen K
authors.initials: KK
authors.lastname: Lindfors
authors.affiliation: University of Oklahoma Health Sciences Center , Department of Radiological Sciences, 940 N.E. 13th Street, Oklahoma City 73104, Oklahoma, United States.
authors.forename: Kai
authors.initials: K
authors.lastname: Yang
authors.affiliation: University of Chicago , Department of Radiology, 5841 S. Maryland Avenue, Chicago 60637, Illinois, United States.
authors.forename: Alexandra
authors.initials: A
authors.lastname: Edwards
keywords: breast computed tomography; breast mass classification; computer-aided diagnosis/detection; image analysis; segmentation