no code implementations • 25 Apr 2024 • Karthik Gopinath, Xiaoling Hu, Malte Hoffmann, Oula Puonti, Juan Eugenio Iglesias
In human neuroimaging studies, atlas registration enables mapping MRI scans to a common coordinate frame, which is necessary to aggregate data from multiple subjects.
no code implementations • 20 Mar 2024 • Xiaoling Hu, Annabel Sorby-Adams, Frederik Barkhof, W Taylor Kimberly, Oula Puonti, Juan Eugenio Iglesias
White matter hyperintensities (WMH) are a hallmark of cerebrovascular disease and multiple sclerosis.
1 code implementation • 10 Mar 2024 • Peirong Liu, Oula Puonti, Annabel Sorby-Adams, William T. Kimberly, Juan E. Iglesias
Remarkable progress has been made by data-driven machine-learning methods in the analysis of MRI scans.
1 code implementation • 30 Jan 2024 • Livia Rodrigues, Martina Bocchetta, Oula Puonti, Douglas Greve, Ana Carolina Londe, Marcondes França, Simone Appenzeller, Juan Eugenio Iglesias, Leticia Rittner
Materials and Methods: We trained our deep learning method, H-synEx, with synthetic images derived from label maps built from ultra-high resolution ex vivo MRI scans, which enables finer-grained manual segmentation when compared with 1mm isometric in vivo images.
no code implementations • 8 Dec 2023 • Pablo Laso, Stefano Cerri, Annabel Sorby-Adams, Jennifer Guo, Farrah Mateen, Philipp Goebl, Jiaming Wu, Peirong Liu, Hongwei Li, Sean I. Young, Benjamin Billot, Oula Puonti, Gordon Sze, Sam Payabavash, Adam DeHavenon, Kevin N. Sheth, Matthew S. Rosen, John Kirsch, Nicola Strisciuglio, Jelmer M. Wolterink, Arman Eshaghi, Frederik Barkhof, W. Taylor Kimberly, Juan Eugenio Iglesias
Brain atrophy and white matter hyperintensity (WMH) are critical neuroimaging features for ascertaining brain injury in cerebrovascular disease and multiple sclerosis.
1 code implementation • 28 Nov 2023 • Peirong Liu, Oula Puonti, Xiaoling Hu, Daniel C. Alexander, Juan E. Iglesias
We present new metrics to validate the intra- and inter-subject robustness of Brain-ID features, and evaluate their performance on four downstream applications, covering contrast-independent (anatomy reconstruction/contrast synthesis, brain segmentation), and contrast-dependent (super-resolution, bias field estimation) tasks.
1 code implementation • 19 Jun 2023 • Chiara Mauri, Stefano Cerri, Oula Puonti, Mark Mühlau, Koen van Leemput
Recent years have seen a growing interest in methods for predicting a variable of interest, such as a subject's diagnosis, from medical images.
2 code implementations • 20 Jul 2021 • Benjamin Billot, Douglas N. Greve, Oula Puonti, Axel Thielscher, Koen van Leemput, Bruce Fischl, Adrian V. Dalca, Juan Eugenio Iglesias
Here we introduce SynthSeg, the first segmentation CNN robust against changes in contrast and resolution.
1 code implementation • 11 May 2020 • Stefano Cerri, Oula Puonti, Dominik S. Meier, Jens Wuerfel, Mark Mühlau, Hartwig R. Siebner, Koen van Leemput
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 sclerosis patients.
no code implementations • 18 Jul 2018 • Mikael Agn, Per Munck af Rosenschöld, Oula Puonti, Michael J. Lundemann, Laura Mancini, Anastasia Papadaki, Steffi Thust, John Ashburner, Ian Law, Koen van Leemput
In this paper we present a method for simultaneously segmenting brain tumors and an extensive set of organs-at-risk for radiation therapy planning of glioblastomas.