An improved FSL-FIRST pipeline for subcortical gray matter segmentation to study abnormal brain anatomy using quantitative susceptibility mapping (QSM) |
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| Affiliation: | 1. Medical Physics Group, Institute of Diagnostic and Interventional Radiology, Jena University Hospital - Friedrich Schiller University Jena, Jena, Germany;2. Section of Experimental Neurology, Department of Neurology, Essen University Hospital, Essen, Germany;3. Erwin L. Hahn Institute for Magnetic Resonance Imaging, University Duisburg-Essen, Essen, Germany;4. Buffalo Neuroimaging Analysis Center, Dept. of Neurology, Jacobs School of Medicine and Biomedical Sciences, State University of New York at Buffalo, Buffalo, NY, United States;5. UNATI, CEA DRF/I2BM, INSERM, Université Paris-Sud, Université Paris-Saclay, NeuroSpin Center, Gif/Yvette, France;6. Inserm/CEA/Université Paris Sud/CNRS, CEA/I2BM/SHFJ, Laboratoire IMIV, Orsay, France;7. MRI Molecular and Translational Imaging Center, Buffalo CTRC, State University of New York at Buffalo, Buffalo, NY, United States;8. Center of Medical Optics and Photonics, Friedrich Schiller University Jena, Germany;9. Michael Stifel Center for Data-driven and Simulation Science Jena, Friedrich Schiller University Jena, Germany;1. Department of Radiology, Weill Cornell Medical College, New York, NY, United States;2. Department of Neurology, Weill Cornell Medical College, New York, NY, United States;1. Department of Imaging and Interventional Radiology, Faculty of Medicine, The Chinese University of Hong Kong, Prince of Wales Hospital, Shatin, New Territories, Hong Kong SAR;2. Department of Radiology, Shenzhen No. 3 People''s Hospital, Shenzhen, Guangdong Province, China;3. MR Clinical Sciences, Philips Healthcare Greater China, Beijing, China;4. Shenzhen Center for Chronic Disease Control, Shenzhen, Guangdong Province, China;1. Department of Radiology and Nuclear Medicine, VU University Medical Center, Amsterdam, The Netherlands;2. NMR Research Unit, Queen Square Multiple Sclerosis Centre, University College London (UCL) Institute of Neurology, London, UK;3. National Institute for Health Research (NIHR), University College London Hospitals (UCLH), Biomedical Research Centre, London, UK;4. Department of Physics and Medical Technology, VU University Medical Center, Amsterdam, The Netherlands;1. FMRIB Centre, Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, United Kingdom;2. Amsterdam Brain and Cognition, University of Amsterdam, Amsterdam, Netherlands;3. Commissariat à l''Energie Atomique et aux Energies Alternatives (CEA), Département des Sciences du Vivant (DSV), Institut d''Imagerie Biomédicale (I2BM), MIRCen, F-92260 Fontenay-aux-Roses, France;4. Centre National de la Recherche Scientifique (CNRS), Université Paris-Sud, Université Paris-Saclay, UMR 9199, Neurodegenerative Diseases Laboratory, F-92260 Fontenay-aux-Roses, France;5. AP-HP, Hôpital Henri Mondor, Centre de Référence-Maladie de Huntington, Neurologie cognitive, Créteil, France;6. Université Paris Est, Faculté de médecine, Créteil, France;7. INSERM U955, Equipe 01, Neuropsychologie Interventionnelle, Créteil, France;8. Département d''Etudes Cognitives, Ecole Normale Supérieure, PSL Research University, Paris, France;9. Centre Expert Parkinson et NEURATRIS, CHU Henri Mondor, Pôle Neuro-Locomoteur, Assistance Publique Hôpitaux de Paris et Université Paris Est Créteil, France |
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| Abstract: | Accurate and robust segmentation of subcortical gray matter (SGM) nuclei is required in many neuroimaging applications. FMRIB's Integrated Registration and Segmentation Tool (FIRST) is one of the most popular software tools for automated subcortical segmentation based on T1-weighted (T1w) images. In this work, we demonstrate that FIRST tends to produce inaccurate SGM segmentation results in the case of abnormal brain anatomy, such as present in atrophied brains, due to a poor spatial match of the subcortical structures with the training data in the MNI space as well as due to insufficient contrast of SGM structures on T1w images. Consequently, such deviations from the average brain anatomy may introduce analysis bias in clinical studies, which may not always be obvious and potentially remain unidentified. To improve the segmentation of subcortical nuclei, we propose to use FIRST in combination with a special Hybrid image Contrast (HC) and Non-Linear (nl) registration module (HC-nlFIRST), where the hybrid image contrast is derived from T1w images and magnetic susceptibility maps to create subcortical contrast that is similar to that in the Montreal Neurological Institute (MNI) template. In our approach, a nonlinear registration replaces FIRST's default linear registration, yielding a more accurate alignment of the input data to the MNI template. We evaluated our method on 82 subjects with particularly abnormal brain anatomy, selected from a database of > 2000 clinical cases. Qualitative and quantitative analyses revealed that HC-nlFIRST provides improved segmentation compared to the default FIRST method. |
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