Multi-atlas image registration of clinical data with automated quality assessment using ventricle segmentation |
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| Affiliation: | 1. J. Philip Kistler Stroke Research Center, Department of Neurology, Massachusetts General Hospital, Harvard Medical School, Boston, USA;2. Biomedical Imaging Group Rotterdam, Department of Radiology and Nuclear Medicine, Erasmus MC - University Medical Center Rotterdam, The Netherlands;3. Department of Computer Science, University of Copenhagen, Copenhagen, Denmark;4. Computer Science and Artificial Intelligence Lab, Massachusetts Institute of Technology, Cambridge, USA;5. Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Charlestown, MA, USA;6. Department of Epidemiology, Erasmus MC - University Medical Center Rotterdam, the Netherlands;7. Department of Imaging Physics, Faculty of Applied Science, TU Delft, Delft, The Netherlands;8. Department of Radiology and Nuclear Medicine, Erasmus MC - University Medical Center Rotterdam, the Netherlands;9. Department of Population Health Sciences, German Centre for Neurodegenerative Diseases (DZNE), Germany;1. Key Laboratory of Intelligent Computing and Signal Processing of Ministry of Education, Anhui University, China;2. School of Computer Science and Technology, Anhui University, China;3. Department of Gastroenterology, The First Affiliated Hospital of Anhui Medical University, Anhui, China;4. School of Health Science, Western University, London, ON N6A 3K7, Canada;5. Department of Medical Imaging, Western University, London, ON N6A 3K7, Canada;1. School of Computer Science and Technology, Harbin Institute of Technology, Harbin, 150001, China;2. The Department of Radiology, The Fourth Hospital of Harbin Medical University, Harbin 150001, China;3. School of Physics and Astronomy, University of Manchester, Manchester, UK;4. The Department of Medical Imaging, Western University, London, Canada;5. The Digital Imaging Group of London, London, ON N6A 3K7, Canada;1. Basque Center on Cognition, Brain and Language (BCBL), Spain;2. A.A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Harvard Medical School, USA;1. Université Paris Descartes, Université Sorbonne Paris Cité (USPC), France;2. Faculté de Pharmacie de Paris - EA bioSTM, France;3. Université Pierre et Marie Curie - Sorbonne Université, France;4. Hôpital Européen Georges Pompidou (HEGP), Assistance Publiques - Hôpitaux de Paris (APHP), France;5. UMR-S970, PARCC, France;6. Hôpital Tenon - APHP, France;7. ®;8. MAP5, UMR CNRS 8145, France;1. Biocomputing Research Center, School of Computer Science and Technology, Harbin Institute of Technology, Harbin, China;2. The Department of Radiology, The Fourth Hospital of Harbin Medical University, China;3. School of Physics and Astronomy, University of Manchester, Manchester, UK;4. The Department of Medical Imaging, Western University, London, Canada;5. The Digital Imaging Group of London, London, ON N6A 3K7, Canada |
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| Abstract: | Registration is a core component of many imaging pipelines. In case of clinical scans, with lower resolution and sometimes substantial motion artifacts, registration can produce poor results. Visual assessment of registration quality in large clinical datasets is inefficient. In this work, we propose to automatically assess the quality of registration to an atlas in clinical FLAIR MRI scans of the brain. The method consists of automatically segmenting the ventricles of a given scan using a neural network, and comparing the segmentation to the atlas ventricles propagated to image space. We used the proposed method to improve clinical image registration to a general atlas by computing multiple registrations - one directly to the general atlas and others via different age-specific atlases - and then selecting the registration that yielded the highest ventricle overlap. Finally, as an example application of the complete pipeline, a voxelwise map of white matter hyperintensity burden was computed using only the scans with registration quality above a predefined threshold. Methods were evaluated in a single-site dataset of more than 1000 scans, as well as a multi-center dataset comprising 142 clinical scans from 12 sites. The automated ventricle segmentation reached a Dice coefficient with manual annotations of 0.89 in the single-site dataset, and 0.83 in the multi-center dataset. Registration via age-specific atlases could improve ventricle overlap compared to a direct registration to the general atlas (Dice similarity coefficient increase up to 0.15). Experiments also showed that selecting scans with the registration quality assessment method could improve the quality of average maps of white matter hyperintensity burden, instead of using all scans for the computation of the white matter hyperintensity map. In this work, we demonstrated the utility of an automated tool for assessing image registration quality in clinical scans. This image quality assessment step could ultimately assist in the translation of automated neuroimaging pipelines to the clinic. |
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