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Brainlesion (2017). 2018;10670:3-14. doi: 10.1007/978-3-319-75238-9_1. Epub 2018 Feb 17.

Dice Overlap Measures for Objects of Unknown Number: Application to Lesion Segmentation.

Author information

1
Department of Radiology, University of Pennsylvania, Philadelphia, PA 19104, USA.
2
Department of Electrical and Computer Engineering, The Johns Hopkins University, Baltimore, MD 21218, USA.
3
Department of Computer Science, The Johns Hopkins University, Baltimore, MD 21218, USA.
4
CNRM, The Henry M. Jackson Foundation for the Advancement of Military Medicine, Bethesda, MD 20817, USA.
5
Department of Neurology, The Johns Hopkins University School of Medicine, Baltimore, MD 21287, USA.
6
Department of Biostatistics and Epidemiology, University of Pennsylvania, Philadelphia, PA 19104, USA.

Abstract

The Dice overlap ratio is commonly used to evaluate the performance of image segmentation algorithms. While Dice overlap is very useful as a standardized quantitative measure of segmentation accuracy in many applications, it offers a very limited picture of segmentation quality in complex segmentation tasks where the number of target objects is not known a priori, such as the segmentation of white matter lesions or lung nodules. While Dice overlap can still be used in these applications, segmentation algorithms may perform quite differently in ways not reflected by differences in their Dice score. Here we propose a new set of evaluation techniques that offer new insights into the behavior of segmentation algorithms. We illustrate these techniques with a case study comparing two popular multiple sclerosis (MS) lesion segmentation algorithms: OASIS and LesionTOADS.

KEYWORDS:

Evaluation; Lesion; MS; Segmentation

PMID:
29714358
PMCID:
PMC5920690
[Available on 2019-02-17]
DOI:
10.1007/978-3-319-75238-9_1

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