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Tayebi Arasteh S, Romanowicz J, Pace DF, Golland P, Powell AJ, Maier AK, Truhn D, Brosch T, Weese J, Lotfinia M, van der Geest RJ, Moghari MH. Automated segmentation of 3D cine cardiovascular magnetic resonance imaging. Front Cardiovasc Med 2023; 10:1167500. [PMID: 37904806 PMCID: PMC10613522 DOI: 10.3389/fcvm.2023.1167500] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/16/2023] [Accepted: 09/18/2023] [Indexed: 11/01/2023] Open
Abstract
Introduction As the life expectancy of children with congenital heart disease (CHD) is rapidly increasing and the adult population with CHD is growing, there is an unmet need to improve clinical workflow and efficiency of analysis. Cardiovascular magnetic resonance (CMR) is a noninvasive imaging modality for monitoring patients with CHD. CMR exam is based on multiple breath-hold 2-dimensional (2D) cine acquisitions that should be precisely prescribed and is expert and institution dependent. Moreover, 2D cine images have relatively thick slices, which does not allow for isotropic delineation of ventricular structures. Thus, development of an isotropic 3D cine acquisition and automatic segmentation method is worthwhile to make CMR workflow straightforward and efficient, as the present work aims to establish. Methods Ninety-nine patients with many types of CHD were imaged using a non-angulated 3D cine CMR sequence covering the whole-heart and great vessels. Automatic supervised and semi-supervised deep-learning-based methods were developed for whole-heart segmentation of 3D cine images to separately delineate the cardiac structures, including both atria, both ventricles, aorta, pulmonary arteries, and superior and inferior vena cavae. The segmentation results derived from the two methods were compared with the manual segmentation in terms of Dice score, a degree of overlap agreement, and atrial and ventricular volume measurements. Results The semi-supervised method resulted in a better overlap agreement with the manual segmentation than the supervised method for all 8 structures (Dice score 83.23 ± 16.76% vs. 77.98 ± 19.64%; P-value ≤0.001). The mean difference error in atrial and ventricular volumetric measurements between manual segmentation and semi-supervised method was lower (bias ≤ 5.2 ml) than the supervised method (bias ≤ 10.1 ml). Discussion The proposed semi-supervised method is capable of cardiac segmentation and chamber volume quantification in a CHD population with wide anatomical variability. It accurately delineates the heart chambers and great vessels and can be used to accurately calculate ventricular and atrial volumes throughout the cardiac cycle. Such a segmentation method can reduce inter- and intra- observer variability and make CMR exams more standardized and efficient.
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Affiliation(s)
- Soroosh Tayebi Arasteh
- Department of Cardiology, Boston Children’s Hospital, and Department of Pediatrics, Harvard Medical School, Boston, MA, United States
- Pattern Recognition Lab, Friedrich-Alexander-Universität Erlangen-Nürnberg, Erlangen, Germany
- Department of Diagnostic and Interventional Radiology, University Hospital RWTH Aachen, Aachen, Germany
| | - Jennifer Romanowicz
- Department of Cardiology, Boston Children’s Hospital, and Department of Pediatrics, Harvard Medical School, Boston, MA, United States
- Department of Cardiology, Children’s Hospital Colorado, and School of Medicine, University of Colorado, Aurora, CO, United States
| | - Danielle F. Pace
- Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, MA, United States
- Computer Science & Artificial Intelligence Lab, Massachusetts Institute of Technology, Cambridge, MA, United States
| | - Polina Golland
- Computer Science & Artificial Intelligence Lab, Massachusetts Institute of Technology, Cambridge, MA, United States
| | - Andrew J. Powell
- Department of Cardiology, Boston Children’s Hospital, and Department of Pediatrics, Harvard Medical School, Boston, MA, United States
| | - Andreas K. Maier
- Pattern Recognition Lab, Friedrich-Alexander-Universität Erlangen-Nürnberg, Erlangen, Germany
| | - Daniel Truhn
- Department of Diagnostic and Interventional Radiology, University Hospital RWTH Aachen, Aachen, Germany
| | - Tom Brosch
- Philips Research Laboratories, Hamburg, Germany
| | | | - Mahshad Lotfinia
- Institute of Heat and Mass Transfer, RWTH Aachen University, Aachen, Germany
| | | | - Mehdi H. Moghari
- Department of Radiology, Children’s Hospital Colorado, and School of Medicine, University of Colorado, Aurora, CO, United States
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Adeli E, Li X, Kwon D, Zhang Y, Pohl KM. Logistic Regression Confined by Cardinality-Constrained Sample and Feature Selection. IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE 2020; 42:1713-1728. [PMID: 30835210 PMCID: PMC7331794 DOI: 10.1109/tpami.2019.2901688] [Citation(s) in RCA: 14] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/09/2023]
Abstract
Many vision-based applications rely on logistic regression for embedding classification within a probabilistic context, such as recognition in images and videos or identifying disease-specific image phenotypes from neuroimages. Logistic regression, however, often performs poorly when trained on data that is noisy, has irrelevant features, or when the samples are distributed across the classes in an imbalanced setting; a common occurrence in visual recognition tasks. To deal with those issues, researchers generally rely on ad-hoc regularization techniques or model a subset of these issues. We instead propose a mathematically sound logistic regression model that selects a subset of (relevant) features and (informative and balanced) set of samples during the training process. The model does so by applying cardinality constraints (via l0-'norm' sparsity) on the features and samples. l0 defines sparsity in mathematical settings but in practice has mostly been approximated (e.g., via l1 or its variations) for computational simplicity. We prove that a local minimum to the non-convex optimization problems induced by cardinality constraints can be computed by combining block coordinate descent with penalty decomposition. On synthetic, image recognition, and neuroimaging datasets, we show that the accuracy of the method is higher than alternative methods and classifiers commonly used in the literature.
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Affiliation(s)
| | | | - Dongjin Kwon
- Center for Health Sciences, SRI International, Menlo Park, CA, 94025
- Department of Psychiatry and Behavioral Sciences, Stanford University, Stanford, CA, 94305
| | - Yong Zhang
- Vancouver Research Center, Huawei, Burnaby, BC, Canada V5C 6S7
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Mohammed MA, Abdulkareem KH, Al-Waisy AS, Mostafa SA, Al-Fahdawi S, Dinar AM, Alhakami W, Baz A, Al-Mhiqani MN, Alhakami H, Arbaiy N, Maashi MS, Mutlag AA, Garcia-Zapirain B, De La Torre Diez I. Benchmarking Methodology for Selection of Optimal COVID-19 Diagnostic Model Based on Entropy and TOPSIS Methods. IEEE ACCESS 2020; 8:99115-99131. [DOI: 10.1109/access.2020.2995597] [Citation(s) in RCA: 57] [Impact Index Per Article: 14.3] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 08/29/2023]
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Adeli E, Zahr NM, Pfefferbaum A, Sullivan EV, Pohl KM. Novel Machine Learning Identifies Brain Patterns Distinguishing Diagnostic Membership of Human Immunodeficiency Virus, Alcoholism, and Their Comorbidity of Individuals. BIOLOGICAL PSYCHIATRY: COGNITIVE NEUROSCIENCE AND NEUROIMAGING 2019; 4:589-599. [PMID: 30982583 DOI: 10.1016/j.bpsc.2019.02.003] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/05/2018] [Revised: 02/15/2019] [Accepted: 02/15/2019] [Indexed: 12/13/2022]
Abstract
The incidence of alcohol use disorder (AUD) in human immunodeficiency virus (HIV) infection is twice that of the rest of the population. This study documents complex radiologically identified, neuroanatomical effects of AUD+HIV comorbidity by identifying structural brain systems that predicted diagnosis on an individual basis. Applying novel machine learning analysis to 549 participants (199 control subjects, 222 with AUD, 68 with HIV, 60 with AUD+HIV), 298 magnetic resonance imaging brain measurements were automatically reduced to small subsets per group. Significance of each diagnostic pattern was inferred from its accuracy in predicting diagnosis and performance on six cognitive measures. While all three diagnostic patterns predicted the learning and memory score, the AUD+HIV pattern was the largest and had the highest predication accuracy (78.1%). Providing a roadmap for analyzing large, multimodal datasets, the machine learning analysis revealed imaging phenotypes that predicted diagnostic membership of magnetic resonance imaging scans of individuals with AUD, HIV, and their comorbidity.
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Affiliation(s)
- Ehsan Adeli
- Department of Psychiatry and Behavioral Sciences, Stanford School of Medicine, Stanford, California
| | - Natalie M Zahr
- Department of Psychiatry and Behavioral Sciences, Stanford School of Medicine, Stanford, California; Center for Biomedical Sciences, SRI International, Menlo Park, California
| | - Adolf Pfefferbaum
- Department of Psychiatry and Behavioral Sciences, Stanford School of Medicine, Stanford, California; Center for Biomedical Sciences, SRI International, Menlo Park, California
| | - Edith V Sullivan
- Department of Psychiatry and Behavioral Sciences, Stanford School of Medicine, Stanford, California
| | - Kilian M Pohl
- Department of Psychiatry and Behavioral Sciences, Stanford School of Medicine, Stanford, California; Center for Biomedical Sciences, SRI International, Menlo Park, California.
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Joint Data Harmonization and Group Cardinality Constrained Classification. MEDICAL IMAGE COMPUTING AND COMPUTER-ASSISTED INTERVENTION : MICCAI ... INTERNATIONAL CONFERENCE ON MEDICAL IMAGE COMPUTING AND COMPUTER-ASSISTED INTERVENTION 2016; 9900:282-290. [PMID: 28758167 DOI: 10.1007/978-3-319-46720-7_33] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/24/2022]
Abstract
To boost the power of classifiers, studies often increase the size of existing samples through the addition of independently collected data sets. Doing so requires harmonizing the data for demographic and acquisition differences based on a control cohort before performing disease specific classification. The initial harmonization often mitigates group differences negatively impacting classification accuracy. To preserve cohort separation, we propose the first model unifying linear regression for data harmonization with a logistic regression for disease classification. Learning to harmonize data is now an adaptive process taking both disease and control data into account. Solutions within that model are confined by group cardinality to reduce the risk of overfitting (via sparsity), to explicitly account for the impact of disease on the inter-dependency of regions (by grouping them), and to identify disease specific patterns (by enforcing sparsity via the l0-'norm'). We test those solutions in distinguishing HIV-Associated Neurocognitive Disorder from Mild Cognitive Impairment of two independently collected, neuroimage data sets; each contains controls and samples from one disease. Our classifier is impartial to acquisition difference between the data sets while being more accurate in diseases seperation than sequential learning of harmonization and classification parameters, and non-sparsity based logistic regressors.
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Zhang Y, Kwon D, Esmaeili-Firidouni P, Pfefferbaum A, Sullivan EV, Javitz H, Valcour V, Pohl KM. Extracting patterns of morphometry distinguishing HIV associated neurodegeneration from mild cognitive impairment via group cardinality constrained classification. Hum Brain Mapp 2016; 37:4523-4538. [PMID: 27489003 DOI: 10.1002/hbm.23326] [Citation(s) in RCA: 19] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/08/2016] [Revised: 06/11/2016] [Accepted: 07/19/2016] [Indexed: 01/11/2023] Open
Abstract
HIV-Associated Neurocognitive Disorder (HAND) is the most common constellation of cognitive dysfunctions in chronic HIV infected patients age 60 or older in the U.S. Only few published methods assist in distinguishing HAND from other forms of age-associated cognitive decline, such as Mild Cognitive Impairment (MCI). In this report, a data-driven, nonparameteric model to identify morphometric patterns separating HAND from MCI due to non-HIV conditions in this older age group was proposed. This model enhanced the potential for group separation by combining a smaller, longitudinal data set containing HAND samples with a larger, public data set including MCI cases. Using cross-validation, a linear model on healthy controls to harmonize the volumetric scores extracted from MRIs for demographic and acquisition differences between the two independent, disease-specific data sets was trained. Next, patterns distinguishing HAND from MCI via a group sparsity constrained logistic classifier were identified. Unlike existing approaches, our classifier directly solved the underlying minimization problem by decoupling the minimization of the logistic regression function from enforcing the group sparsity constraint. The extracted patterns consisted of eight regions that distinguished HAND from MCI on a significant level while being indifferent to differences in demographics and acquisition between the two sets. Individually selecting regions through conventional morphometric group analysis resulted in a larger number of regions that were less accurate. In conclusion, simultaneously analyzing all brain regions and time points for disease specific patterns contributed to distinguishing with high accuracy HAND-related impairment from cognitive impairment found in the HIV uninfected, MCI cohort. Hum Brain Mapp 37:4523-4538, 2016. © 2016 Wiley Periodicals, Inc.
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Affiliation(s)
- Yong Zhang
- Department of Psychiatry & Behavioral Sciences, Stanford University, Stanford, California, 94305
| | - Dongjin Kwon
- Department of Psychiatry & Behavioral Sciences, Stanford University, Stanford, California, 94305.,Center for Health Sciences, SRI International, Menlo Park, California, 94025
| | | | - Adolf Pfefferbaum
- Center for Health Sciences, SRI International, Menlo Park, California, 94025
| | - Edith V Sullivan
- Department of Psychiatry & Behavioral Sciences, Stanford University, Stanford, California, 94305
| | - Harold Javitz
- Center for Technology in Learning in the Education Division, SRI International, Menlo Park, California, 94025
| | - Victor Valcour
- Memory and Aging Center, UCSF, San Francisco, California
| | - Kilian M Pohl
- Department of Psychiatry & Behavioral Sciences, Stanford University, Stanford, California, 94305.,Center for Health Sciences, SRI International, Menlo Park, California, 94025
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