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Erschienen in:
Buchtitelbild

2018 | OriginalPaper | Buchkapitel

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

verfasst von : Ipek Oguz, Aaron Carass, Dzung L. Pham, Snehashis Roy, Nagesh Subbana, Peter A. Calabresi, Paul A. Yushkevich, Russell T. Shinohara, Jerry L. Prince

Erschienen in: Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries

Verlag: Springer International Publishing

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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.

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Nächstes Kapitel Lesion Detection, Segmentation and Prediction in Multiple Sclerosis Clinical Trials
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Metadaten
Titel
Dice Overlap Measures for Objects of Unknown Number: Application to Lesion Segmentation
verfasst von
Ipek Oguz
Aaron Carass
Dzung L. Pham
Snehashis Roy
Nagesh Subbana
Peter A. Calabresi
Paul A. Yushkevich
Russell T. Shinohara
Jerry L. Prince
Copyright-Jahr
2018
Buch
Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries

Print ISBN: 978-3-319-75237-2

Electronic ISBN: 978-3-319-75238-9

Copyright-Jahr: 2018

DOI
https://doi.org/10.1007/978-3-319-75238-9_1