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Lai JW, Ang CKE, Acharya UR, Cheong KH. Schizophrenia: A Survey of Artificial Intelligence Techniques Applied to Detection and Classification. INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH 2021; 18:6099. [PMID: 34198829 PMCID: PMC8201065 DOI: 10.3390/ijerph18116099] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 05/01/2021] [Revised: 05/26/2021] [Accepted: 05/28/2021] [Indexed: 02/07/2023]
Abstract
Artificial Intelligence in healthcare employs machine learning algorithms to emulate human cognition in the analysis of complicated or large sets of data. Specifically, artificial intelligence taps on the ability of computer algorithms and software with allowable thresholds to make deterministic approximate conclusions. In comparison to traditional technologies in healthcare, artificial intelligence enhances the process of data analysis without the need for human input, producing nearly equally reliable, well defined output. Schizophrenia is a chronic mental health condition that affects millions worldwide, with impairment in thinking and behaviour that may be significantly disabling to daily living. Multiple artificial intelligence and machine learning algorithms have been utilized to analyze the different components of schizophrenia, such as in prediction of disease, and assessment of current prevention methods. These are carried out in hope of assisting with diagnosis and provision of viable options for individuals affected. In this paper, we review the progress of the use of artificial intelligence in schizophrenia.
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Affiliation(s)
- Joel Weijia Lai
- Science, Mathematics and Technology, Singapore University of Technology and Design, 8 Somapah Road, Singapore 487372, Singapore; (J.W.L.); (C.K.E.A.)
| | - Candice Ke En Ang
- Science, Mathematics and Technology, Singapore University of Technology and Design, 8 Somapah Road, Singapore 487372, Singapore; (J.W.L.); (C.K.E.A.)
- MOH Holdings Pte Ltd, 1 Maritime Square, Singapore 099253, Singapore
| | - U. Rajendra Acharya
- Department of Electronics and Computer Engineering, Ngee Ann Polytechnic, Clementi 599489, Singapore;
- Department of Biomedical Engineering, School of Science and Technology, Singapore University of Social Sciences, Clementi 599491, Singapore
- Department of Biomedical Informatics and Medical Engineering, Asia University, Taichung 41354, Taiwan
| | - Kang Hao Cheong
- Science, Mathematics and Technology, Singapore University of Technology and Design, 8 Somapah Road, Singapore 487372, Singapore; (J.W.L.); (C.K.E.A.)
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Rashid B, Calhoun V. Towards a brain-based predictome of mental illness. Hum Brain Mapp 2020; 41:3468-3535. [PMID: 32374075 PMCID: PMC7375108 DOI: 10.1002/hbm.25013] [Citation(s) in RCA: 65] [Impact Index Per Article: 16.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/21/2019] [Revised: 04/06/2020] [Accepted: 04/06/2020] [Indexed: 01/10/2023] Open
Abstract
Neuroimaging-based approaches have been extensively applied to study mental illness in recent years and have deepened our understanding of both cognitively healthy and disordered brain structure and function. Recent advancements in machine learning techniques have shown promising outcomes for individualized prediction and characterization of patients with psychiatric disorders. Studies have utilized features from a variety of neuroimaging modalities, including structural, functional, and diffusion magnetic resonance imaging data, as well as jointly estimated features from multiple modalities, to assess patients with heterogeneous mental disorders, such as schizophrenia and autism. We use the term "predictome" to describe the use of multivariate brain network features from one or more neuroimaging modalities to predict mental illness. In the predictome, multiple brain network-based features (either from the same modality or multiple modalities) are incorporated into a predictive model to jointly estimate features that are unique to a disorder and predict subjects accordingly. To date, more than 650 studies have been published on subject-level prediction focusing on psychiatric disorders. We have surveyed about 250 studies including schizophrenia, major depression, bipolar disorder, autism spectrum disorder, attention-deficit hyperactivity disorder, obsessive-compulsive disorder, social anxiety disorder, posttraumatic stress disorder, and substance dependence. In this review, we present a comprehensive review of recent neuroimaging-based predictomic approaches, current trends, and common shortcomings and share our vision for future directions.
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Affiliation(s)
- Barnaly Rashid
- Department of PsychiatryHarvard Medical SchoolBostonMassachusettsUSA
| | - Vince Calhoun
- Tri‐Institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS)Georgia State University, Georgia Institute of Technology, and Emory UniversityAtlantaGeorgiaUSA
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3
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A Systematic Characterization of Structural Brain Changes in Schizophrenia. Neurosci Bull 2020; 36:1107-1122. [PMID: 32495122 DOI: 10.1007/s12264-020-00520-8] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/05/2019] [Accepted: 02/13/2020] [Indexed: 01/10/2023] Open
Abstract
A systematic characterization of the similarities and differences among different methods for detecting structural brain abnormalities in schizophrenia, such as voxel-based morphometry (VBM), tensor-based morphometry (TBM), and projection-based thickness (PBT), is important for understanding the brain pathology in schizophrenia and for developing effective biomarkers for a diagnosis of schizophrenia. However, such studies are still lacking. Here, we performed VBM, TBM, and PBT analyses on T1-weighted brain MR images acquired from 116 patients with schizophrenia and 116 healthy controls. We found that, although all methods detected wide-spread structural changes, different methods captured different information - only 10.35% of the grey matter changes in cortex were detected by all three methods, and VBM only detected 11.36% of the white matter changes detected by TBM. Further, pattern classification between patients and controls revealed that combining different measures improved the classification accuracy (81.9%), indicating that fusion of different structural measures serves as a better neuroimaging marker for the objective diagnosis of schizophrenia.
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Winterburn JL, Voineskos AN, Devenyi GA, Plitman E, de la Fuente-Sandoval C, Bhagwat N, Graff-Guerrero A, Knight J, Chakravarty MM. Can we accurately classify schizophrenia patients from healthy controls using magnetic resonance imaging and machine learning? A multi-method and multi-dataset study. Schizophr Res 2019; 214:3-10. [PMID: 29274736 DOI: 10.1016/j.schres.2017.11.038] [Citation(s) in RCA: 33] [Impact Index Per Article: 6.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/02/2017] [Revised: 11/24/2017] [Accepted: 11/29/2017] [Indexed: 10/18/2022]
Abstract
Machine learning is a powerful tool that has previously been used to classify schizophrenia (SZ) patients from healthy controls (HC) using magnetic resonance images. Each study, however, uses different datasets, classification algorithms, and validation techniques. Here, we perform a critical appraisal of the accuracy of machine learning methodologies used in SZ/HC classifications studies by comparing three machine learning algorithms (logistic regression [LR], support vector machines [SVMs], and linear discriminant analysis [LDA]) on three independent datasets (435 subjects total) using two tissue density estimates and cortical thickness (CT). Performance is assessed using 10-fold cross-validation, as well as a held-out validation set. Classification using CT outperformed tissue densities, but there was no clear effect of dataset. LR, SVMs, and LDA each yielded the highest accuracies for a different feature set and validation paradigm, but most accuracies were between 55 and 70%, well below previously reported values. The highest accuracy achieved was 73.5% using CT data and an SVM. Taken together, these results illustrate some of the obstacles to constructing effective disease classifiers, and suggest that tissue densities and CT may not be sufficiently sensitive for SZ/HC classification given current available methodologies and sample sizes.
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Affiliation(s)
- Julie L Winterburn
- Computational Brain Anatomy Laboratory, Douglas Mental Health Institute, McGill University, Montreal, Quebec, Canada; Institute of Biomaterials and Biomedical Engineering, University of Toronto, Toronto, Ontario, Canada; Kimel Family Translational Imaging-Genetics Research Lab, Research Imaging Centre, Campbell Family Mental Health Research Institute, Centre for Addiction and Mental Health, Toronto, Ontario, Canada.
| | - Aristotle N Voineskos
- Kimel Family Translational Imaging-Genetics Research Lab, Research Imaging Centre, Campbell Family Mental Health Research Institute, Centre for Addiction and Mental Health, Toronto, Ontario, Canada; Geriatric Mental Health Division, Centre for Addiction and Mental Health, Toronto, Ontario, Canada; Department of Psychiatry, University of Toronto, Toronto, Ontario, Canada; Institute of Medical Science, University of Toronto, Toronto, Ontario, Canada
| | - Gabriel A Devenyi
- Computational Brain Anatomy Laboratory, Douglas Mental Health Institute, McGill University, Montreal, Quebec, Canada; Department of Psychiatry, McGill University, Montreal, Quebec, Canada
| | - Eric Plitman
- Institute of Medical Science, University of Toronto, Toronto, Ontario, Canada; Multimodal Imaging Group, Research Imaging Centre, Centre for Addiction and Mental Health, Toronto, Ontario, Canada
| | - Camilo de la Fuente-Sandoval
- Laboratory of Experimental Psychiatry, Instituto Nacional de Neurología y Neurocirugía, Mexico City, Mexico; Neuropsychiatry Department, Instituto Nacional de Neurología y Neurocirugía, Mexico City, Mexico
| | - Nikhil Bhagwat
- Computational Brain Anatomy Laboratory, Douglas Mental Health Institute, McGill University, Montreal, Quebec, Canada; Institute of Biomaterials and Biomedical Engineering, University of Toronto, Toronto, Ontario, Canada; Kimel Family Translational Imaging-Genetics Research Lab, Research Imaging Centre, Campbell Family Mental Health Research Institute, Centre for Addiction and Mental Health, Toronto, Ontario, Canada
| | - Ariel Graff-Guerrero
- Geriatric Mental Health Division, Centre for Addiction and Mental Health, Toronto, Ontario, Canada; Department of Psychiatry, University of Toronto, Toronto, Ontario, Canada; Institute of Medical Science, University of Toronto, Toronto, Ontario, Canada; Multimodal Imaging Group, Research Imaging Centre, Centre for Addiction and Mental Health, Toronto, Ontario, Canada
| | - Jo Knight
- Department of Psychiatry, University of Toronto, Toronto, Ontario, Canada; Institute of Medical Science, University of Toronto, Toronto, Ontario, Canada; Data Science Institute and Medical School, Lancaster University, Bailrigg, United Kingdom
| | - M Mallar Chakravarty
- Computational Brain Anatomy Laboratory, Douglas Mental Health Institute, McGill University, Montreal, Quebec, Canada; Institute of Biomaterials and Biomedical Engineering, University of Toronto, Toronto, Ontario, Canada; Department of Psychiatry, McGill University, Montreal, Quebec, Canada; Biological and Biomedical Engineering, McGill University, Montreal, Quebec, Canada.
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Kim J, Valdés Hernández MDC, Royle NA, Maniega SM, Aribisala BS, Gow AJ, Bastin ME, Deary IJ, Wardlaw JM, Park J. 3D shape analysis of the brain's third ventricle using a midplane encoded symmetric template model. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2016; 129:51-62. [PMID: 27084320 PMCID: PMC4841787 DOI: 10.1016/j.cmpb.2016.02.014] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 11/04/2015] [Revised: 01/12/2016] [Accepted: 02/22/2016] [Indexed: 06/05/2023]
Abstract
BACKGROUND Structural changes of the brain's third ventricle have been acknowledged as an indicative measure of the brain atrophy progression in neurodegenerative and endocrinal diseases. To investigate the ventricular enlargement in relation to the atrophy of the surrounding structures, shape analysis is a promising approach. However, there are hurdles in modeling the third ventricle shape. First, it has topological variations across individuals due to the inter-thalamic adhesion. In addition, as an interhemispheric structure, it needs to be aligned to the midsagittal plane to assess its asymmetric and regional deformation. METHOD To address these issues, we propose a model-based shape assessment. Our template model of the third ventricle consists of a midplane and a symmetric mesh of generic shape. By mapping the template's midplane to the individuals' brain midsagittal plane, we align the symmetric mesh on the midline of the brain before quantifying the third ventricle shape. To build the vertex-wise correspondence between the individual third ventricle and the template mesh, we employ a minimal-distortion surface deformation framework. In addition, to account for topological variations, we implement geometric constraints guiding the template mesh to have zero width where the inter-thalamic adhesion passes through, preventing vertices crossing between left and right walls of the third ventricle. The individual shapes are compared using a vertex-wise deformity from the symmetric template. RESULTS Experiments on imaging and demographic data from a study of aging showed that our model was sensitive in assessing morphological differences between individuals in relation to brain volume (i.e. proxy for general brain atrophy), gender and the fluid intelligence at age 72. It also revealed that the proposed method can detect the regional and asymmetrical deformation unlike the conventional measures: volume (median 1.95ml, IQR 0.96ml) and width of the third ventricle. Similarity measures between binary masks and the shape model showed that the latter reconstructed shape details with high accuracy (Dice coefficient ≥0.9, mean distance 0.5mm and Hausdorff distance 2.7mm). CONCLUSIONS We have demonstrated that our approach is suitable to morphometrical analyses of the third ventricle, providing high accuracy and inter-subject consistency in the shape quantification. This shape modeling method with geometric constraints based on anatomical landmarks could be extended to other brain structures which require a consistent measurement basis in the morphometry.
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Affiliation(s)
- Jaeil Kim
- School of Computing, Korea Advanced Institute of Science and Technology, Daejeon, South Korea
| | - Maria del C Valdés Hernández
- Brain Research Imaging Centre, Department of Neuroimaging Sciences, University of Edinburgh, Edinburgh, UK; Centre for Cognitive Ageing and Cognitive Epidemiology, University of Edinburgh, Edinburgh, UK; SINAPSE (Scottish Imaging Network, A Platform for Scientific Excellence) Collaboration, Scotland, UK
| | - Natalie A Royle
- Brain Research Imaging Centre, Department of Neuroimaging Sciences, University of Edinburgh, Edinburgh, UK; Centre for Cognitive Ageing and Cognitive Epidemiology, University of Edinburgh, Edinburgh, UK; SINAPSE (Scottish Imaging Network, A Platform for Scientific Excellence) Collaboration, Scotland, UK
| | - Susana Muñoz Maniega
- Brain Research Imaging Centre, Department of Neuroimaging Sciences, University of Edinburgh, Edinburgh, UK; Centre for Cognitive Ageing and Cognitive Epidemiology, University of Edinburgh, Edinburgh, UK; SINAPSE (Scottish Imaging Network, A Platform for Scientific Excellence) Collaboration, Scotland, UK
| | - Benjamin S Aribisala
- Brain Research Imaging Centre, Department of Neuroimaging Sciences, University of Edinburgh, Edinburgh, UK; Centre for Cognitive Ageing and Cognitive Epidemiology, University of Edinburgh, Edinburgh, UK; SINAPSE (Scottish Imaging Network, A Platform for Scientific Excellence) Collaboration, Scotland, UK; Computer Science Department, Lagos State University, Nigeria
| | - Alan J Gow
- Centre for Cognitive Ageing and Cognitive Epidemiology, University of Edinburgh, Edinburgh, UK; Psychology, School of Life Sciences, Heriot-Watt University, Edinburgh, UK
| | - Mark E Bastin
- Brain Research Imaging Centre, Department of Neuroimaging Sciences, University of Edinburgh, Edinburgh, UK; Centre for Cognitive Ageing and Cognitive Epidemiology, University of Edinburgh, Edinburgh, UK; SINAPSE (Scottish Imaging Network, A Platform for Scientific Excellence) Collaboration, Scotland, UK
| | - Ian J Deary
- Centre for Cognitive Ageing and Cognitive Epidemiology, University of Edinburgh, Edinburgh, UK; SINAPSE (Scottish Imaging Network, A Platform for Scientific Excellence) Collaboration, Scotland, UK; Department of Psychology, University of Edinburgh, Edinburgh, UK
| | - Joanna M Wardlaw
- Brain Research Imaging Centre, Department of Neuroimaging Sciences, University of Edinburgh, Edinburgh, UK; Centre for Cognitive Ageing and Cognitive Epidemiology, University of Edinburgh, Edinburgh, UK; SINAPSE (Scottish Imaging Network, A Platform for Scientific Excellence) Collaboration, Scotland, UK
| | - Jinah Park
- School of Computing, Korea Advanced Institute of Science and Technology, Daejeon, South Korea.
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Wolfers T, Buitelaar JK, Beckmann CF, Franke B, Marquand AF. From estimating activation locality to predicting disorder: A review of pattern recognition for neuroimaging-based psychiatric diagnostics. Neurosci Biobehav Rev 2015; 57:328-49. [PMID: 26254595 DOI: 10.1016/j.neubiorev.2015.08.001] [Citation(s) in RCA: 183] [Impact Index Per Article: 20.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/18/2015] [Revised: 07/29/2015] [Accepted: 08/02/2015] [Indexed: 12/19/2022]
Abstract
Psychiatric disorders are increasingly being recognised as having a biological basis, but their diagnosis is made exclusively behaviourally. A promising approach for 'biomarker' discovery has been based on pattern recognition methods applied to neuroimaging data, which could yield clinical utility in future. In this review we survey the literature on pattern recognition for making diagnostic predictions in psychiatric disorders, and evaluate progress made in translating such findings towards clinical application. We evaluate studies on many criteria, including data modalities used, the types of features extracted and algorithm applied. We identify problems common to many studies, such as a relatively small sample size and a primary focus on estimating generalisability within a single study. Furthermore, we highlight challenges that are not widely acknowledged in the field including the importance of accommodating disease prevalence, the necessity of more extensive validation using large carefully acquired samples, the need for methodological innovations to improve accuracy and to discriminate between multiple disorders simultaneously. Finally, we identify specific clinical contexts in which pattern recognition can add value in the short to medium term.
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Affiliation(s)
- Thomas Wolfers
- Department of Human Genetics, Donders Institute for Brain, Cognition and Behaviour, Radboud University Medical Center, PO Box 9101, 6500 HB Nijmegen, The Netherlands; Donders Centre for Cognitive Neuroimaging, Donders Institute for Brain, Cognition and Behaviour, Radboud University, PO Box 9101, 6500 HB Nijmegen, The Netherlands.
| | - Jan K Buitelaar
- Department of Cognitive Neuroscience, Donders Institute for Brain, Cognition and Behaviour, Radboud University Medical Centre, Nijmegen, The Netherlands; Karakter Child and Adolescent Psychiatry University Centre, Radboud University Medical Centre, PO Box 9101, 6500 HB Nijmegen, The Netherlands
| | - Christian F Beckmann
- Donders Centre for Cognitive Neuroimaging, Donders Institute for Brain, Cognition and Behaviour, Radboud University, PO Box 9101, 6500 HB Nijmegen, The Netherlands; Department of Cognitive Neuroscience, Donders Institute for Brain, Cognition and Behaviour, Radboud University Medical Centre, Nijmegen, The Netherlands; Centre for Functional MRI of the Brain (FMRIB), University of Oxford, Oxford, United Kingdom
| | - Barbara Franke
- Department of Human Genetics, Donders Institute for Brain, Cognition and Behaviour, Radboud University Medical Center, PO Box 9101, 6500 HB Nijmegen, The Netherlands; Department of Psychiatry, Donders Institute for Brain, Cognition and Behaviour, Radboud University Medical Center, Nijmegen, The Netherlands
| | - Andre F Marquand
- Donders Centre for Cognitive Neuroimaging, Donders Institute for Brain, Cognition and Behaviour, Radboud University, PO Box 9101, 6500 HB Nijmegen, The Netherlands; Department of Neuroimaging, Institute of Psychiatry, King's College London, LondonUnited Kingdom
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7
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[Neuroimaging in psychiatry: multivariate analysis techniques for diagnosis and prognosis]. DER NERVENARZT 2014; 85:714-9. [PMID: 24849118 DOI: 10.1007/s00115-014-4022-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 10/25/2022]
Abstract
BACKGROUND Multiple studies successfully applied multivariate analysis to neuroimaging data demonstrating the potential utility of neuroimaging for clinical diagnostic and prognostic purposes. OBJECTIVES Summary of the current state of research regarding the application of neuroimaging in the field of psychiatry. MATERIAL AND METHODS Literature review of current studies. RESULTS Results of current studies indicate the potential application of neuroimaging data across various diagnoses, such as depression, schizophrenia, bipolar disorder and dementia. Potential applications include disease classification, differential diagnosis and prediction of disease course. CONCLUSION The results of the studies are heterogeneous although some studies report promising findings. Further multicentre studies are needed with clearly specified patient populations to systematically investigate the potential utility of neuroimaging for the clinical routine.
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Yotter RA, Nenadic I, Ziegler G, Thompson PM, Gaser C. Local cortical surface complexity maps from spherical harmonic reconstructions. Neuroimage 2011; 56:961-73. [PMID: 21315159 DOI: 10.1016/j.neuroimage.2011.02.007] [Citation(s) in RCA: 148] [Impact Index Per Article: 11.4] [Reference Citation Analysis] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/08/2010] [Revised: 02/01/2011] [Accepted: 02/01/2011] [Indexed: 01/29/2023] Open
Affiliation(s)
- Rachel A Yotter
- Department of Psychiatry, Friedrich-Schiller University, Jena, Germany.
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Abstract
Cortical surface complexity is a potential structural marker for certain diseases such as schizophrenia. In this study, we developed a measure of fractal dimension (FD) calculated from lowpass-filtered spherical harmonic brain surface reconstructions. A local FD measure was also computed at each vertex in a cortical surface mesh, visualizing local variations in surface complexity over the brain surface. We analyzed the surface complexity for 87 patients with DSM-IV schizophrenia (with stable psychopathology and treated with antipsychotic medication) and 108 matched healthy controls. The global FD for the right hemisphere in the schizophrenic group was significantly lower than that in controls. Local FD maps showed that the lower complexity was mainly due to differences in the prefrontal cortex.
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Nenadic I, Sauer H, Gaser C. Distinct pattern of brain structural deficits in subsyndromes of schizophrenia delineated by psychopathology. Neuroimage 2010; 49:1153-60. [PMID: 19833216 DOI: 10.1016/j.neuroimage.2009.10.014] [Citation(s) in RCA: 68] [Impact Index Per Article: 4.9] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/18/2009] [Revised: 09/08/2009] [Accepted: 10/06/2009] [Indexed: 12/17/2022] Open
Affiliation(s)
- Igor Nenadic
- Department of Psychiatry, Friedrich-Schiller-University of Jena, Philosophenweg 3, D-07743 Jena, Germany.
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Evaluation of shape-based normalization in the corpus callosum for white matter connectivity analysis. MEDICAL IMAGE COMPUTING AND COMPUTER-ASSISTED INTERVENTION : MICCAI ... INTERNATIONAL CONFERENCE ON MEDICAL IMAGE COMPUTING AND COMPUTER-ASSISTED INTERVENTION 2007. [PMID: 18044639 DOI: 10.1007/978-3-540-75759-7_94] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register]
Abstract
Recently, concerns have been raised that the correspondences computed by volumetric registration within homogeneous structures are primarily driven by regularization priors that differ among algorithms. This paper explores the correspondence based on geometric models for one of those structures, midsagittal section of the corpus callosum (MSCC), and compared the result with registration paradigms. We use geometric model called continuous medial representation (cm-rep) to normalize anatomical structures on the basis of medial geometry, and use features derived from diffusion tensor tractography for validation. We show that shape-based normalization aligns subregions of the MSCC, defined by connectivity, more accurately than normalization based on volumetric registration. Furthermore, shape-based normalization helps increase the statistical power of group analysis in an experiment where features derived from diffusion tensor tractography are compared between two cohorts. These results suggest that cm-rep is an appropriate tool for normalizing the MSCC in white matter studies.
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Sun H, Yushkevich PA, Zhang H, Cook PA, Duda JT, Simon TJ, Gee JC. Shape-based normalization of the corpus callosum for DTI connectivity analysis. IEEE TRANSACTIONS ON MEDICAL IMAGING 2007; 26:1166-78. [PMID: 17896590 DOI: 10.1109/tmi.2007.900322] [Citation(s) in RCA: 13] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/17/2023]
Abstract
The continuous medial representation (cm-rep) is an approach that makes it possible to model, normalize, and analyze anatomical structures on the basis of medial geometry. Having recently presented a partial differential equation (PDE)-based approach for 3-D cm-rep modeling [1], here we present an equivalent 2-D approach that involves solving an ordinary differential equation. This paper derives a closed form solution of this equation and shows how Pythagorean hodograph curves can be used to express the solution as a piecewise polynomial function, allowing efficient and robust medial modeling. The utility of the approach in medical image analysis is demonstrated by applying it to the problem of shape-based normalization of the midsagittal section of the corpus callosum. Using diffusion tensor tractography, we show that shape-based normalization aligns subregions of the corpus callosum, defined by connectivity, more accurately than normalization based on volumetric registration. Furthermore, shape-based normalization helps increase the statistical power of group analysis in an experiment where features derived from diffusion tensor tractography are compared between two cohorts. These results suggest that cm-rep is an appropriate tool for normalizing the corpus callosum in white matter studies.
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Affiliation(s)
- Hui Sun
- Penn Image Computing and Science Laboratory, Department of Radiology, University of Pennsylvania, Philadelphia, PA 19104, USA .
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Staib LH, Styner M. Advances in radiologic image analysis from MICCAI 2005. Acad Radiol 2006; 13:1053-4. [PMID: 16935716 DOI: 10.1016/j.acra.2006.06.013] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/22/2006] [Revised: 06/16/2006] [Accepted: 06/22/2006] [Indexed: 11/20/2022]
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Yushkevich PA, Piven J, Hazlett HC, Smith RG, Ho S, Gee JC, Gerig G. User-guided 3D active contour segmentation of anatomical structures: Significantly improved efficiency and reliability. Neuroimage 2006. [DOI: 10.1016/j.neuroimage.2006.01.015 order by 8029-- #] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/10/2023] Open
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Yushkevich PA, Piven J, Hazlett HC, Smith RG, Ho S, Gee JC, Gerig G. User-guided 3D active contour segmentation of anatomical structures: Significantly improved efficiency and reliability. Neuroimage 2006. [DOI: 10.1016/j.neuroimage.2006.01.015 order by 1-- #] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/10/2023] Open
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Yushkevich PA, Piven J, Hazlett HC, Smith RG, Ho S, Gee JC, Gerig G. User-guided 3D active contour segmentation of anatomical structures: Significantly improved efficiency and reliability. Neuroimage 2006. [DOI: 10.1016/j.neuroimage.2006.01.015 and 1880=1880] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/10/2023] Open
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17
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User-guided 3D active contour segmentation of anatomical structures: Significantly improved efficiency and reliability. Neuroimage 2006. [DOI: 10.1016/j.neuroimage.2006.01.015 order by 8029-- -] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/10/2023] Open
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18
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Yushkevich PA, Piven J, Hazlett HC, Smith RG, Ho S, Gee JC, Gerig G. User-guided 3D active contour segmentation of anatomical structures: Significantly improved efficiency and reliability. Neuroimage 2006. [DOI: 10.1016/j.neuroimage.2006.01.015 order by 1-- -] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/10/2023] Open
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Yushkevich PA, Piven J, Hazlett HC, Smith RG, Ho S, Gee JC, Gerig G. User-guided 3D active contour segmentation of anatomical structures: Significantly improved efficiency and reliability. Neuroimage 2006. [DOI: 10.1016/j.neuroimage.2006.01.015 order by 1-- gadu] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/10/2023] Open
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20
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User-guided 3D active contour segmentation of anatomical structures: Significantly improved efficiency and reliability. Neuroimage 2006. [DOI: 10.1016/j.neuroimage.2006.01.015 order by 8029-- awyx] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/10/2023] Open
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Yushkevich PA, Piven J, Hazlett HC, Smith RG, Ho S, Gee JC, Gerig G. User-guided 3D active contour segmentation of anatomical structures: significantly improved efficiency and reliability. Neuroimage 2006; 31:1116-28. [PMID: 16545965 DOI: 10.1016/j.neuroimage.2006.01.015] [Citation(s) in RCA: 5328] [Impact Index Per Article: 296.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/29/2005] [Revised: 12/21/2005] [Accepted: 01/24/2006] [Indexed: 02/07/2023] Open
Abstract
Active contour segmentation and its robust implementation using level set methods are well-established theoretical approaches that have been studied thoroughly in the image analysis literature. Despite the existence of these powerful segmentation methods, the needs of clinical research continue to be fulfilled, to a large extent, using slice-by-slice manual tracing. To bridge the gap between methodological advances and clinical routine, we developed an open source application called ITK-SNAP, which is intended to make level set segmentation easily accessible to a wide range of users, including those with little or no mathematical expertise. This paper describes the methods and software engineering philosophy behind this new tool and provides the results of validation experiments performed in the context of an ongoing child autism neuroimaging study. The validation establishes SNAP intrarater and interrater reliability and overlap error statistics for the caudate nucleus and finds that SNAP is a highly reliable and efficient alternative to manual tracing. Analogous results for lateral ventricle segmentation are provided.
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Affiliation(s)
- Paul A Yushkevich
- Penn Image Computing and Science Laboratory, Department of Radiology, University of Pennsylvania, PA 19104-6274, USA.
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Haynor DR, Barillot C, Hellier P. A selection of papers from MICCAI 2004: the marriage of data and prior information. Acad Radiol 2005; 12:1229-31. [PMID: 16179199 DOI: 10.1016/j.acra.2005.06.016] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/29/2005] [Revised: 06/29/2005] [Accepted: 06/29/2005] [Indexed: 10/25/2022]
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