An automated tool for detection of FLAIR-hyperintense white-matter lesions in Multiple Sclerosis
By P. Schmidt, Christian Gaser, M. Arsic, D. Buck, A. Förschler, A. Berthele, M. Hoshi, R. Ilg, Volker J Schmid, C. Zimmer and 2 more. NeuroImage, 2012
1,198 citations recorded by the source corpus.
Subfields: Pathology and Forensic Medicine, Radiology, Nuclear Medicine and Imaging, Rheumatology
View Paper: https://www.semanticscholar.org/paper/fd26a0e43b5ab1d68f55176a0651a88353a2e085
How researchers see this paper
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In Pathology and Forensic Medicine
In Radiology, Nuclear Medicine and Imaging
- Sergi Valverde
- Mariano Cabezas
- Xavier Lladó
- Arnau Oliver
- Mark Jenkinson
- Peter M. Rothwell
- Ludovica Griffanti
- Stephen M. Smith
Citations in the corpus
- Deep 3D Convolutional Encoder Networks With Shortcuts for Multiscale Feature Integration Applied to Multiple Sclerosis Lesion Segmentation (2016, methodology): “, a subject-specific generative model of tissue intensities [32], [34], [41], [43], or a generative model of image patches representing a healthy population [44].”
- Improving automated multiple sclerosis lesion segmentation with a cascaded 3D convolutional neural network approach (2017, methodology): “SLS and LST methods are designed to reduce the F P R , so as a counterpart they exhibit a higher number of missed lesions (depicted as blue squares).”
- BIANCA (Brain Intensity AbNormality Classification Algorithm): A new tool for automated segmentation of white matter hyperintensities (2016, methodology): “After fi nding the optimal threshold for CASCADE, and the optimal initial threshold (kappa) value for LGA, we compared the performance of the three algorithms (in terms of overlap and volumetric agreement with the manual masks) against the optimal results from BIANCA.”
- Longitudinal multiple sclerosis lesion segmentation: Resource and challenge (2017, neutral): “…et al., 2015; Jog et al., 2015; Johnston et al., 1996; Kamber et al., 1996; Khayati et al., 2008; Rey et al., 1999, 2002; Roy et al., 2010, 2014b; Schmidt et al., 2012; Shiee et al., 2010; Subbanna et al., 2015; Sudre et al., 2015; Tomas-Fernandez and Warfield, 2011, 2012; Valverde et al., 2017;…”
- Evaluation of a deep learning approach for the segmentation of brain tissues and white matter hyperintensities of presumed vascular origin in MRI (2017, neutral): “We have compared the method to two publicly available methods, LST (Schmidt et al., 2012) and a cascaded CNN (Valverde et al., 2017).”