1 code implementation • 28 Mar 2024 • Ezequiel de la Rosa, Mauricio Reyes, Sook-Lei Liew, Alexandre Hutton, Roland Wiest, Johannes Kaesmacher, Uta Hanning, Arsany Hakim, Richard Zubal, Waldo Valenzuela, David Robben, Diana M. Sima, Vincenzo Anania, Arne Brys, James A. Meakin, Anne Mickan, Gabriel Broocks, Christian Heitkamp, Shengbo Gao, Kongming Liang, Ziji Zhang, Md Mahfuzur Rahman Siddiquee, Andriy Myronenko, Pooya Ashtari, Sabine Van Huffel, Hyun-su Jeong, Chi-ho Yoon, Chulhong Kim, Jiayu Huo, Sebastien Ourselin, Rachel Sparks, Albert Clèrigues, Arnau Oliver, Xavier Lladó, Liam Chalcroft, Ioannis Pappas, Jeroen Bertels, Ewout Heylen, Juliette Moreau, Nima Hatami, Carole Frindel, Abdul Qayyum, Moona Mazher, Domenec Puig, Shao-Chieh Lin, Chun-Jung Juan, Tianxi Hu, Lyndon Boone, Maged Goubran, Yi-Jui Liu, Susanne Wegener, Florian Kofler, Ivan Ezhov, Suprosanna Shit, Moritz R. Hernandez Petzsche, Bjoern Menze, Jan S. Kirschke, Benedikt Wiestler
We address this gap by presenting a novel ensemble algorithm derived from the 2022 Ischemic Stroke Lesion Segmentation (ISLES) challenge.
2 code implementations • 24 Feb 2022 • Pooya Ashtari, Diana M. Sima, Lieven De Lathauwer, Dominique Sappey-Marinier, Frederik Maes, Sabine Van Huffel
Specifically, we propose a linearly scalable approach to context modeling, formulating Nonnegative Matrix Factorization (NMF) as a differentiable layer integrated into a U-shaped architecture.
no code implementations • 31 Mar 2021 • Ezequiel de la Rosa, David Robben, Diana M. Sima, Jan S. Kirschke, Bjoern Menze
We show that our approach is able to generate AIFs without any manual annotation, and hence avoiding manual rater's influences.
1 code implementation • 23 Mar 2021 • Maria Ines Meyer, Ezequiel de la Rosa, Nuno Barros, Roberto Paolella, Koen van Leemput, Diana M. Sima
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-center and multi-scanner data.
no code implementations • 9 Oct 2020 • Jaime Simarro, Ezequiel de la Rosa, Thijs Vande Vyvere, David Robben, Diana M. Sima
Moreover, we test the potential of the method for detecting other anomalies such as low quality images, preprocessing inaccuracies, artifacts, and even the presence of post-operative signs (such as a craniectomy or a brain shunt).
no code implementations • 4 Oct 2020 • Ezequiel de la Rosa, Diana M. Sima, Bjoern Menze, Jan S. Kirschke, David Robben
Perfusion imaging is crucial in acute ischemic stroke for quantifying the salvageable penumbra and irreversibly damaged core lesions.
no code implementations • 3 Feb 2020 • Mattias Billast, Maria Ines Meyer, Diana M. Sima, David Robben
A discriminator model is then trained to predict if two lesion segmentations are based on scans acquired using the same scanner type or not, achieving a 78% accuracy in this task.
no code implementations • 8 Nov 2019 • Maria Ines Meyer, Ezequiel de la Rosa, Koen van Leemput, Diana M. Sima
In this work, we explore a novel approach to harmonize brain volume measurements by using only image descriptors.
no code implementations • 14 Nov 2018 • Ezequiel de la Rosa, Diana M. Sima, Thijs Vande Vyvere, Jan S. Kirschke, Bjoern Menze
Relevant shape, intensity and texture biomarkers characterizing the different lesions are isolated and a lesion predictive model is built by using Partial Least Squares.