164 citations · 190 across the 9 of their papers we have counts for
11 papers
BINDER: A Latent Variable Model for Probabilistic Medical Image Registration
Stefano Cerri, Amirhossein Hassankhani, Yaël Balbastre +1
We propose a new probabilistic model for general-purpose medical image registration that builds upon the mutual information registration criterion. It centers around a spatial inte…
Towards Brain MRI Foundation Models for the Clinic: Findings from the FOMO25 Challenge
Asbjørn Munk, Stefano Cerri, Vardan Nersesjan +81
Clinical deployment of automated brain MRI analysis faces a fundamental challenge: clinical data is heterogeneous and noisy, and high-quality labels are prohibitively costly to obt…
A large-scale heterogeneous 3D magnetic resonance brain imaging dataset for self-supervised learning
Stefano Cerri, Asbjørn Munk, Sebastian Nørgaard Llambias +11
We present FOMO260K, a large-scale, heterogeneous dataset of 260,927 brain Magnetic Resonance Imaging (MRI) scans from 77,589 MRI sessions and 55,378 subjects, aggregated from 910…
Quantifying white matter hyperintensity and brain volumes in heterogeneous clinical and low-field portable MRI
Pablo Laso, Stefano Cerri, Annabel Sorby-Adams +21
Brain atrophy and white matter hyperintensity (WMH) are critical neuroimaging features for ascertaining brain injury in cerebrovascular disease and multiple sclerosis. Automated se…
A Lightweight Generative Model for Interpretable Subject-level Prediction
Chiara Mauri, Stefano Cerri, Oula Puonti +2
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-fun…
An Open-Source Tool for Longitudinal Whole-Brain and White Matter Lesion Segmentation
Stefano Cerri, Douglas N. Greve, Andrew Hoopes +4
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…
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…