Zobrazeno 1 - 10
of 272
pro vyhledávání: '"Cerri, Stefano A"'
Autor:
Laso, Pablo, Cerri, Stefano, Sorby-Adams, Annabel, Guo, Jennifer, Mateen, Farrah, Goebl, Philipp, Wu, Jiaming, Liu, Peirong, Li, Hongwei, Young, Sean I., Billot, Benjamin, Puonti, Oula, Sze, Gordon, Payabavash, Sam, DeHavenon, Adam, Sheth, Kevin N., Rosen, Matthew S., Kirsch, John, Strisciuglio, Nicola, Wolterink, Jelmer M., Eshaghi, Arman, Barkhof, Frederik, Kimberly, W. Taylor, Iglesias, Juan Eugenio
Brain atrophy and white matter hyperintensity (WMH) are critical neuroimaging features for ascertaining brain injury in cerebrovascular disease and multiple sclerosis. Automated segmentation and quantification is desirable but existing methods requir
Externí odkaz:
http://arxiv.org/abs/2312.05119
Recent years have seen a growing interest in methods for predicting an unknown variable of interest, such as a subject's diagnosis, from medical images depicting its anatomical-functional effects. Methods based on discriminative modeling excel at mak
Externí odkaz:
http://arxiv.org/abs/2306.11107
Autor:
Cerri, Stefano, Greve, Douglas N., Hoopes, Andrew, Lundell, Henrik, Siebner, Hartwig R., Mühlau, Mark, Van Leemput, Koen
In this paper we describe and validate a longitudinal method for whole-brain segmentation of longitudinal MRI scans. It builds upon an existing whole-brain segmentation method that can handle multi-contrast data and robustly analyze images with white
Externí odkaz:
http://arxiv.org/abs/2207.04534
In this paper we propose a method for predicting the status of MGMT promoter methylation in high-grade gliomas. From the available MR images, we segment the tumor using deep convolutional neural networks and extract both radiomic features and shape f
Externí odkaz:
http://arxiv.org/abs/2109.12339
Autor:
Pálsson, Sveinn, Cerri, Stefano, Poulsen, Hans Skovgaard, Urup, Thomas, Law, Ian, Van Leemput, Koen
Survival prediction models can potentially be used to guide treatment of glioblastoma patients. However, currently available MR imaging biomarkers holding prognostic information are often challenging to interpret, have difficulties generalizing acros
Externí odkaz:
http://arxiv.org/abs/2109.12334
In this paper we propose a novel method for the segmentation of longitudinal brain MRI scans of patients suffering from Multiple Sclerosis. The method builds upon an existing cross-sectional method for simultaneous whole-brain and lesion segmentation
Externí odkaz:
http://arxiv.org/abs/2008.05117
Autor:
Cerri, Stefano, Puonti, Oula, Meier, Dominik S., Wuerfel, Jens, Mühlau, Mark, Siebner, Hartwig R., Van Leemput, Koen
Here we present a method for the simultaneous segmentation of white matter lesions and normal-appearing neuroanatomical structures from multi-contrast brain MRI scans of multiple sclerosis patients. The method integrates a novel model for white matte
Externí odkaz:
http://arxiv.org/abs/2005.05135
In this paper we propose a semi-supervised variational autoencoder for classification of overall survival groups from tumor segmentation masks. The model can use the output of any tumor segmentation algorithm, removing all assumptions on the scanning
Externí odkaz:
http://arxiv.org/abs/1910.04488
Autor:
Cerri, Stefano, Greve, Douglas N., Hoopes, Andrew, Lundell, Henrik, Siebner, Hartwig R., Mühlau, Mark, Van Leemput, Koen
Publikováno v:
In NeuroImage: Clinical 2023 38
