Improving automated multiple sclerosis lesion segmentation with a cascaded 3D convolutional neural network approach
dc.contributor.author
dc.date.accessioned
2017-10-24T10:46:07Z
dc.date.available
2017-10-24T10:46:07Z
dc.date.issued
2017-07-15
dc.identifier.issn
1053-8119
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dc.description.abstract
In this paper, we present a novel automated method for White Matter (WM) lesion segmentation of Multiple Sclerosis (MS) patient images. Our approach is based on a cascade of two 3D patch-wise convolutional neural networks (CNN). The first network is trained to be more sensitive revealing possible candidate lesion voxels while the second network is trained to reduce the number of misclassified voxels coming from the first network. This cascaded CNN architecture tends to learn well from a small (n≤35) set of labeled data of the same MRI contrast, which can be very interesting in practice, given the difficulty to obtain manual label annotations and the large amount of available unlabeled Magnetic Resonance Imaging (MRI) data. We evaluate the accuracy of the proposed method on the public MS lesion segmentation challenge MICCAI2008 dataset, comparing it with respect to other state-of-the-art MS lesion segmentation tools. Furthermore, the proposed method is also evaluated on two private MS clinical datasets, where the performance of our method is also compared with different recent public available state-of-the-art MS lesion segmentation methods. At the time of writing this paper, our method is the best ranked approach on the MICCAI2008 challenge, outperforming the rest of 60 participant methods when using all the available input modalities (T1-w, T2-w and FLAIR), while still in the top-rank (3rd position) when using only T1-w and FLAIR modalities. On clinical MS data, our approach exhibits a significant increase in the accuracy segmenting of WM lesions when compared with the rest of evaluated methods, highly correlating (r≥0.97) also with the expected lesion volume
dc.description.sponsorship
This work has been partially supported by “La Fundació la Marató de TV3”, by Retos de InvestigaciónTIN2014-55710-R, and by the MPC UdG 2016/022 grant
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application/pdf
dc.language.iso
eng
dc.publisher
Elsevier
dc.relation
info:eu-repo/grantAgreement/MINECO//TIN2014-55710-R/ES/HERRAMIENTAS DE NEUROIMAGEN PARA MEJORAR EL DIAGNOSIS Y EL SEGUIMIENTO CLINICO DE LOS PACIENTES CON ESCLEROSIS MULTIPLE/
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Reproducció digital del document publicat a: https://doi.org/10.1016/j.neuroimage.2017.04.034
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© NeuroImage, 2017, vol. 155, p. 159-168
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Articles publicats (D-ATC)
dc.rights
Tots els drets reservats
dc.subject
dc.title
Improving automated multiple sclerosis lesion segmentation with a cascaded 3D convolutional neural network approach
dc.type
info:eu-repo/semantics/article
dc.rights.accessRights
info:eu-repo/semantics/embargoedAccess
dc.embargo.terms
Cap
dc.date.embargoEndDate
info:eu-repo/date/embargoEnd/2026-01-01
dc.type.version
info:eu-repo/semantics/publishedVersion
dc.identifier.doi
dc.identifier.idgrec
026695
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