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Publication Type : Conference Paper
Thematic Areas : Wireless Network and Application
Publisher : International MICCAI Brain Lesion Workshop.
Source : International MICCAI Brain Lesion Workshop, pp. 3-14, MICCAI 2017.
Keywords : Evaluation; Lesion; MS; Segmentation.
Campus : Amritapuri
School : School of Engineering
Center : Amrita Center for Wireless Networks and Applications (AmritaWNA)
Department : Wireless Networks and Applications (AWNA)
Year : 2017
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.
Cite this Research Publication : I. Oguz, A. Carass, D. L. Pham, S. Roy, N. K. Subbanna, P. A. Calabresi, P. A. Yushkevich, R. T. Shinohara, J. L. Prince, "Dice Overlap Measures for Objects of Unknown Number: Application to Lesion Segmentation", International MICCAI Brain Lesion Workshop, pp. 3-14, MICCAI 2017. doi: 10.1007/978-3-319-75238-9_1