5 papers
Prediction of MGMT Methylation Status of Glioblastoma using Radiomics and Latent Space Shape Features
Sveinn Pálsson, Stefano Cerri, Koen Van Leemput
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 convol…
Predicting survival of glioblastoma from automatic whole-brain and tumor segmentation of MR images
Sveinn Pálsson, Stefano Cerri, Hans Skovgaard Poulsen +3
Survival prediction models can potentially be used to guide treatment of glioblastoma patients. However, currently available MR imaging biomarkers holding prognostic information ar…
A Longitudinal Method for Simultaneous Whole-Brain and Lesion Segmentation in Multiple Sclerosis
Stefano Cerri, Andrew Hoopes, Douglas N. Greve +2
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…
A Contrast-Adaptive Method for Simultaneous Whole-Brain and Lesion Segmentation in Multiple Sclerosis
Stefano Cerri, Oula Puonti, Dominik S. Meier +4
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 s…
Semi-Supervised Variational Autoencoder for Survival Prediction
Sveinn Pálsson, Stefano Cerri, Andrea Dittadi +1
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…