3 papers
eess.IV2021
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…
eess.IV2021
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…
cs.CV2019
Joint inference on structural and diffusion MRI for sequence-adaptive Bayesian segmentation of thalamic nuclei with probabilistic atlases
Juan Eugenio Iglesias, Koen Van Leemput, Polina Golland +1
Segmentation of structural and diffusion MRI (sMRI/dMRI) is usually performed independently in neuroimaging pipelines. However, some brain structures (e.g., globus pallidus, thalam…