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A hybrid approach based on logistic classification and iterative contrast enhancement algorithm for hyperintense multiple sclerosis lesion segmentation

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Abstract

Multiple sclerosis (MS) is a neurodegenerative disease with increasing importance in recent years, in which the T2 weighted with fluid attenuation inversion recovery (FLAIR) MRI imaging technique has been addressed for the hyperintense MS lesion assessment. Many automatic lesion segmentation approaches have been proposed in the literature in order to assist health professionals. In this study, a new hybrid lesion segmentation approach based on logistic classification (LC) and the iterative contrast enhancement (ICE) method is proposed (LC+ICE). T1 and FLAIR MRI images from 32 secondary progressive MS (SPMS) patients were used in the LC+ICE method, in which manual segmentation was used as the ground truth lesion segmentation. The DICE, Sensitivity, Specificity, Area under the ROC curve (AUC), and Volume Similarity measures showed that the LC+ICE method is able to provide a precise and robust lesion segmentation estimate, which was compared with two recent FLAIR lesion segmentation approaches. In addition, the proposed method also showed a stable segmentation among lesion loads, showing a wide applicability to different disease stages. The LC+ICE procedure is a suitable alternative to assist the manual FLAIR hyperintense MS lesion segmentation task.

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Notes

  1. https://portal.fli-iam.irisa.fr/msseg-challenge/overview

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Funding

The author would like to thank Conselho Nacional de Desenvolvimento Cientifico e Tecnologico (CNPq) grant 201871/2015-7/SWE for the financial support.

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  1. Av. Bandeirantes, 3900, Ribeirao Preto, Brazil

    Antonio Carlos da Silva Senra Filho

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Correspondence to Antonio Carlos da Silva Senra Filho.

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da Silva Senra Filho, A.C. A hybrid approach based on logistic classification and iterative contrast enhancement algorithm for hyperintense multiple sclerosis lesion segmentation. Med Biol Eng Comput 56, 1063–1076 (2018). https://doi.org/10.1007/s11517-017-1747-2

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