Learning a CNN on multiple sclerosis lesion segmentation with self-supervision
نویسندگان
چکیده
منابع مشابه
Longitudinal multiple sclerosis lesion segmentation data resource
The data presented in this article is related to the research article entitled "Longitudinal multiple sclerosis lesion segmentation: Resource and challenge" (Carass et al., 2017) [1]. In conjunction with the 2015 International Symposium on Biomedical Imaging, we organized a longitudinal multiple sclerosis (MS) lesion segmentation challenge providing training and test data to registered particip...
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Accurate and consistent multiple sclerosis (MS) brain lesion segmentation and volumetry could be an added value to MS clinicians. In this paper, MSmetrix is presented, an automatic and reliable method, which uses 3D T1-weighted and FLAIR MR images in a probabilistic model to detect white matter lesions as an outlier with respect to the normal brain, while segmenting the brain tissue into grey m...
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PURPOSE To automatically segment multiple sclerosis (MS) lesions into three subtypes (i.e., enhancing lesions, T1 "black holes", T2 hyperintense lesions). MATERIALS AND METHODS Proton density-, T2- and contrast-enhanced T1-weighted brain images of 12 MR scans were pre-processed through intracranial cavity (IC) extraction, inhomogeneity correction and intensity normalization. Intensity-based s...
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Background: Multiple Sclerosis (MS) is a degenerative disease of central nervous system. MS patients have some dead tissues in their brains called MS lesions. MRI is an imaging technique sensitive to soft tissues such as brain that shows MS lesions as hyper-intense or hypo-intense signals. Since manual segmentation of these lesions is a laborious and time consuming task, automatic segmentation ...
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We present our entry for the Longitudinal Multiple Sclerosis Challenge 2015 using 3D convolutional neural networks (CNN). We model a voxel-wise classifier using multi-channel 3D patches of MRI volumes as input. For each ground truth, a CNN is trained and the final segmentation is obtained by combining the probability outputs of these CNNs. Efficient training is achieved by using sub-sampling me...
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ژورنال
عنوان ژورنال: Electronic Imaging
سال: 2020
ISSN: 2470-1173
DOI: 10.2352/issn.2470-1173.2020.17.3dmp-002