| Urgent challenges in quantification and interpretation of brain grey matter atrophy in individual MS patients using MRI H Amiri, A de Sitter, K Bendfeldt, M Battaglini, CAMG Wheeler-Kingshott, ... NeuroImage: Clinical 19, 466-475, 2018 | 61 | 2018 |
| Agreement of MSmetrix with established methods for measuring cross-sectional and longitudinal brain atrophy MD Steenwijk, H Amiri, MM Schoonheim, A De Sitter, F Barkhof, ... NeuroImage: Clinical 15, 843-853, 2017 | 46 | 2017 |
| Performance of five research-domain automated WM lesion segmentation methods in a multi-center MS study A de Sitter, MD Steenwijk, A Ruet, A Versteeg, Y Liu, RA van Schijndel, ... NeuroImage 163, 106-114, 2017 | 38 | 2017 |
| Facing privacy in neuroimaging: Removing facial features degrades performance of image analysis methods A de Sitter, M Visser, I Brouwer, KS Cover, RA van Schijndel, RS Eijgelaar, ... European Radiology, 2019 | 34 | 2019 |
| Manual and automated tissue segmentation confirm the impact of thalamus atrophy on cognition in multiple sclerosis: A multicenter study J Burggraaff, Y Liu, JC Prieto, J Simoes, A de Sitter, S Ruggieri, I Brouwer, ... NeuroImage: Clinical 29, 102549, 2021 | 22 | 2021 |
| Do mathematical model studies settle the controversy on the origin of cardiac synchronous trans-thoracic electrical impedance variations? A systematic review A de Sitter, RM Verdaasdonk, TJC Faes Physiological measurement 37 (9), R88, 2016 | 17 | 2016 |
| Reduced accuracy of MRI deep grey matter segmentation in multiple sclerosis: an evaluation of four automated methods against manual reference segmentations in a multi-center cohort A de Sitter, T Verhoeven, J Burggraaff, Y Liu, J Simoes, S Ruggieri, ... Journal of neurology 267 (12), 3541-3554, 2020 | 14 | 2020 |
| Opportunities for understanding MS mechanisms and progression with MRI using large-scale data sharing and artificial intelligence H Vrenken, M Jenkinson, DL Pham, CRG Guttmann, D Pareto, ... Neurology 97 (21), 989-999, 2021 | 11 | 2021 |
| Development and evaluation of a manual segmentation protocol for deep grey matter in multiple sclerosis: Towards accelerated semi-automated references A de Sitter, J Burggraaff, F Bartel, M Palotai, Y Liu, J Simoes, S Ruggieri, ... NeuroImage: Clinical 30, 102659, 2021 | 3 | 2021 |
| Consistency checks for partial volume correction of ASL perfusion maps JP Kuijer, A De Sitter, MA Binnewijzend, F Barkhof, RM Verdaasdonk Proc Intl Soc Mag Reson Med, 0 | 3 | |
| A distributed platform for making large scale manual reference datasets for MS lesion segmentation S Damangir, A de Sitter, I Brouwer, CRG Guttmann, D Pareto, A Rovira, ... MULTIPLE SCLEROSIS JOURNAL 24, 864-864, 2018 | 1 | 2018 |
| Impact of removing facial features from MR images of MS patients on automatic lesion and atrophy metrics A de Sitter, M Visser, I Brouwer, RA van Schijndel, BMJ Uitdehaag, ... MULTIPLE SCLEROSIS JOURNAL 23, 226-226, 2017 | 1 | 2017 |
| MS-specific deep learning segmentation of deep gray matter K Koopman, I Brouwer, N Cantavella, A de Sitter, F Barkhof, H Vrenken MULTIPLE SCLEROSIS JOURNAL 27 (2_ SUPPL), 449-449, 2021 | | 2021 |
| Creating accurate reference segmentations of deep GM structures in MS patients by fast semi-automated outlining A de Sitter, F Bartel, M Palotai, J Burggraaff, Y Liu, J Simoes, S Ruggieri, ... MULTIPLE SCLEROSIS JOURNAL 24, 620-622, 2018 | | 2018 |
| Lesion simulation software LESIM: a robust and flexible tool for realistic simulation of white matter lesions MM Weeda, A de Sitter, I Brouwer, MM de Boer, RJ van Tuijl, ... | | |
