58 citations · 120 across the 9 of their papers we have counts for
10 papers
A Robust Ensemble Algorithm for Ischemic Stroke Lesion Segmentation: Generalizability and Clinical Utility Beyond the ISLES Challenge
Ezequiel de la Rosa, Mauricio Reyes, Sook-Lei Liew +55
Diffusion-weighted MRI (DWI) is essential for stroke diagnosis, treatment decisions, and prognosis. However, image and disease variability hinder the development of generalizable A…
Comparison of different automatic solutions for resection cavity segmentation in postoperative MRI volumes including longitudinal acquisitions
Luca Canalini, Jan Klein, Nuno Pedrosa de Barros +3
In this work, we compare five deep learning solutions to automatically segment the resection cavity in postoperative MRI. The proposed methods are based on the same 3D U-Net archit…
Factorizer: A Scalable Interpretable Approach to Context Modeling for Medical Image Segmentation
Pooya Ashtari, Diana M. Sima, Lieven De Lathauwer +3
Convolutional Neural Networks (CNNs) with U-shaped architectures have dominated medical image segmentation, which is crucial for various clinical purposes. However, the inherent lo…
Differentiable Deconvolution for Improved Stroke Perfusion Analysis
Ezequiel de la Rosa, David Robben, Diana M. Sima +2
Perfusion imaging is the current gold standard for acute ischemic stroke analysis. It allows quantification of the salvageable and non-salvageable tissue regions (penumbra and core…
An augmentation strategy to mimic multi-scanner variability in MRI
Maria Ines Meyer, Ezequiel de la Rosa, Nuno Barros +3
Most publicly available brain MRI datasets are very homogeneous in terms of scanner and protocols, and it is difficult for models that learn from such data to generalize to multi-c…
AIFNet: Automatic Vascular Function Estimation for Perfusion Analysis Using Deep Learning
Ezequiel de la Rosa, Diana M. Sima, Bjoern Menze +2
Perfusion imaging is crucial in acute ischemic stroke for quantifying the salvageable penumbra and irreversibly damaged core lesions. As such, it helps clinicians to decide on the…