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CCsNeT: Automated Corpus Callosum segmentation using fully convolutional network based on U-Net. Biocybern Biomed Eng 2022. [DOI: 10.1016/j.bbe.2021.12.008] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/14/2022]
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Herrera WG, Pereira M, Bento M, Lapa AT, Appenzeller S, Rittner L. A framework for quality control of corpus callosum segmentation in large-scale studies. J Neurosci Methods 2020; 334:108593. [PMID: 31972183 DOI: 10.1016/j.jneumeth.2020.108593] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/24/2019] [Revised: 01/09/2020] [Accepted: 01/09/2020] [Indexed: 11/17/2022]
Abstract
BACKGROUND The corpus callosum (CC) is the largest white matter structure in the brain, responsible for the interconnection of the brain hemispheres. Its segmentation is a required preliminary step for any posterior analysis, such as parcellation, registration, and feature extraction. In this context, the quality control (QC) of CC segmentation allows studies on large datasets with no human interaction, and the proper usage of available automated and semi-automated algorithms. NEW METHOD We propose a framework for QC of CC segmentation based on the shape signature, computed at 49 distinct resolutions. At each resolution, a support vector machine (SVM) classifier was trained, generating 49 individual classifiers. Then, a disagreement metric was used to cluster these individual classifiers. The final ensemble was constructed by selecting one representation from each cluster. RESULTS The proposed framework achieved an area under the curve (AUC) metric of 98.25% on the test set (207 subjects) employing an ensemble composed of 12 components. This ensemble outperformed all individual classifiers. COMPARISON WITH EXISTING METHODS To the best of our knowledge, this is the first approach to assess quality of CC segmentations on large datasets without the need for a ground-truth. CONCLUSIONS The shape descriptor is robust and versatile, describing the segmentation at different resolutions. The selection of classifiers and the disagreement measure lead to an ensemble composed of high-quality and heterogeneous classifiers, ensuring an optimal trade-off between the ensemble size and high AUC.
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Affiliation(s)
- William Garcia Herrera
- Medical Image Computing Laboratory (MICLab), School of Electrical and Computer Engineering, University of Campinas (UNICAMP), Brazil.
| | - Mariana Pereira
- Medical Image Computing Laboratory (MICLab), School of Electrical and Computer Engineering, University of Campinas (UNICAMP), Brazil
| | - Mariana Bento
- Radiology and Clinical Neuroscience, Hotchkiss Brain Institute, University of Calgary, Canada
| | - Aline Tamires Lapa
- Rheumatology Department, Faculty of Medical Science, University of Campinas (UNICAMP), Brazil
| | - Simone Appenzeller
- Rheumatology Department, Faculty of Medical Science, University of Campinas (UNICAMP), Brazil
| | - Leticia Rittner
- Medical Image Computing Laboratory (MICLab), School of Electrical and Computer Engineering, University of Campinas (UNICAMP), Brazil
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Park G, Kwak K, Seo SW, Lee JM. Automatic Segmentation of Corpus Callosum in Midsagittal Based on Bayesian Inference Consisting of Sparse Representation Error and Multi-Atlas Voting. Front Neurosci 2018; 12:629. [PMID: 30271320 PMCID: PMC6142891 DOI: 10.3389/fnins.2018.00629] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/06/2018] [Accepted: 08/21/2018] [Indexed: 11/13/2022] Open
Abstract
In this paper, we introduce a novel automatic method for Corpus Callosum (CC) in midsagittal plane segmentation. The robust segmentation of CC in midsagittal plane is key role for quantitative study of structural features of CC associated with various neurological disorder such as epilepsy, autism, Alzheimer's disease, and so on. Our approach is based on Bayesian inference using sparse representation and multi-atlas voting which both methods are used in various medical imaging, and show outstanding performance. Prior information in the proposed Bayesian inference is obtained from probability map generated from multi-atlas voting. The probability map contains the information of shape and location of CC of target image. Likelihood in the proposed Bayesian inference is obtained from gamma distribution function, generated from reconstruction errors (or sparse representation error), which are calculated in sparse representation of target patch using foreground dictionary and background dictionary each. Unlike the usual sparse representation method, we added gradient magnitude and gradient direction information to the patches of dictionaries and target, which had better segmentation performance than when not added. We compared three main segmentation results as follow: (1) the joint label fusion (JLF) method which is state-of-art method in multi-atlas voting based segmentation for evaluation of our method; (2) prior information estimated from multi-atlas voting only; (3) likelihood estimated from comparison of the reconstruction errors from sparse representation error only; (4) the proposed Bayesian inference. The methods were evaluated using two data sets of T1-weighted images, which one data set consists of 100 normal young subjects and the other data set consist of 25 normal old subjects and 22 old subjects with heavy drinker. In both data sets, the proposed Bayesian inference method has significantly the best segmentation performance than using each method separately.
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Affiliation(s)
- Gilsoon Park
- Department of Biomedical Engineering, Hanyang University, Seoul, South Korea
| | - Kichang Kwak
- McGill Centre for Integrative Neuroscience, Montreal Neurological Institute, McGill University, Montreal, QC, Canada
| | - Sang Won Seo
- Department of Neurology, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, South Korea
| | - Jong-Min Lee
- Department of Biomedical Engineering, Hanyang University, Seoul, South Korea
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Cover GS, Herrera WG, Bento MP, Appenzeller S, Rittner L. Computational methods for corpus callosum segmentation on MRI: A systematic literature review. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2018; 154:25-35. [PMID: 29249344 DOI: 10.1016/j.cmpb.2017.10.025] [Citation(s) in RCA: 15] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/25/2016] [Revised: 10/23/2017] [Accepted: 10/30/2017] [Indexed: 06/07/2023]
Abstract
BACKGROUND AND OBJECTIVE The corpus callosum (CC) is the largest white matter structure in the brain and has a significant role in central nervous system diseases. Its volume correlates with the severity and/or extent of neurodegenerative disease. Even though the CC's role has been extensively studied over the last decades, and different algorithms and methods have been published regarding CC segmentation and parcellation, no reviews or surveys covering such developments have been reported so far. To bridge this gap, this paper presents a systematic literature review of computational methods focusing on CC segmentation and parcellation acquired on magnetic resonance imaging. METHODS IEEExplore, PubMed, EBSCO Host, and Scopus database were searched with the following search terms: ((Segmentation OR Parcellation) AND (Corpus Callosum) AND (DTI OR MRI OR Diffusion Tensor Imag* OR Diffusion Tractography OR Magnetic Resonance Imag*)), resulting in 802 publications. Two reviewers independently evaluated all articles and 36 studies were selected through the systematic literature review process. RESULTS This work reviewed four main segmentation methods groups: model-based, region-based, thresholding, and machine learning; 32 different validity metrics were reported. Even though model-based techniques are the most recurrently used for the segmentation task (13 articles), machine learning approaches achieved better outcomes of 95% when analyzing mean values for segmentation and classification metrics results. Moreover, CC segmentation is better established in T1-weighted images, having more methods implemented and also being tested in larger datasets, compared with diffusion tensor images. CONCLUSIONS The analyzed computational methods used to perform CC segmentation on magnetic resonance imaging have not yet overcome all presented challenges owing to metrics variability and lack of traceable materials.
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Affiliation(s)
- G S Cover
- MICLab - Medical Image Computing Laboratory, School of Electrical and Computer Engineering, University of Campinas, Brazil.
| | - W G Herrera
- MICLab - Medical Image Computing Laboratory, School of Electrical and Computer Engineering, University of Campinas, Brazil
| | - M P Bento
- MICLab - Medical Image Computing Laboratory, School of Electrical and Computer Engineering, University of Campinas, Brazil
| | - S Appenzeller
- Rheumatology Division, Faculty of Medical Science, University of Campinas, Brazil
| | - L Rittner
- MICLab - Medical Image Computing Laboratory, School of Electrical and Computer Engineering, University of Campinas, Brazil
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Farhangi MM, Frigui H, Bert R, Amini AA. Incorporating shape prior into active contours with a sparse linear combination of training shapes: application to corpus callosum segmentation. ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2016; 2016:6449-6452. [PMID: 28269723 DOI: 10.1109/embc.2016.7592205] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/06/2023]
Abstract
In this paper, a novel method of embedding shape information into level set image segmentation is proposed. Our method is based on inferring shape variations by a sparse linear combination of instances in the shape repository. Given a sufficient number of training shapes with variations, a new shape can be approximated by a linear span of training shapes associated with those variations. At each step of curve evolution the curve is moved to minimize Chan-Vese energy functional as well as toward the best approximation based on a linear combination of training samples. Although the method is general, in this paper it has been applied to the problem of segmentation of corpus callosum from 2D sagittal MR images.
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Yang C, Wang Q, Wu W, Xue Y, Lu W, Wu S. Thalamic segmentation based on improved fuzzy connectedness in structural MRI. Comput Biol Med 2015; 66:222-34. [PMID: 26433197 DOI: 10.1016/j.compbiomed.2015.09.002] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/13/2015] [Revised: 08/26/2015] [Accepted: 09/02/2015] [Indexed: 10/23/2022]
Abstract
Thalamic segmentation serves an important function in localizing targets for deep brain stimulation (DBS). However, thalamic nuclei are still difficult to identify clearly from structural MRI. In this study, an improved algorithm based on the fuzzy connectedness framework was developed. Three-dimensional T1-weighted images in axial orientation were acquired through a 3D SPGR sequence by using a 1.5 T GE magnetic resonance scanner. Twenty-five normal images were analyzed using the proposed method, which involved adaptive fuzzy connectedness combined with confidence connectedness (AFCCC). After non-brain tissue removal and contrast enhancement, the seed point was selected manually, and confidence connectedness was used to perform an ROI update automatically. Both image intensity and local gradient were taken as image features in calculating the fuzzy affinity. Moreover, the weight of the features could be automatically adjusted. Thalamus, ventrointermedius (Vim), and subthalamic nucleus were successfully segmented. The results were evaluated with rules, such as similarity degree (SD), union overlap, and false positive. SD of thalamus segmentation reached values higher than 85%. The segmentation results were also compared with those achieved by the region growing and level set methods, respectively. Higher SD of the proposed method, especially in Vim, was achieved. The time cost using AFCCC was low, although it could achieve high accuracy. The proposed method is superior to the traditional fuzzy connectedness framework and involves reduced manual intervention in time saving.
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Affiliation(s)
- Chunlan Yang
- College of Life Science and Bioengineering, Beijing University of Technology, Beijing 100022, China.
| | - Qian Wang
- College of Life Science and Bioengineering, Beijing University of Technology, Beijing 100022, China
| | - Weiwei Wu
- College of Life Science and Bioengineering, Beijing University of Technology, Beijing 100022, China
| | - Yanqing Xue
- Department of Radiotherapy, Beijing Geriatric Hospital, Beijing 100095, China
| | - Wangsheng Lu
- Center of Neurosurgery, PLA NAVY General Hospital, Beijing 100037, China
| | - Shuicai Wu
- College of Life Science and Bioengineering, Beijing University of Technology, Beijing 100022, China
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Gu Y, Kumar V, Hall LO, Goldgof DB, Li CY, Korn R, Bendtsen C, Velazquez ER, Dekker A, Aerts H, Lambin P, Li X, Tian J, Gatenby RA, Gillies RJ. Automated Delineation of Lung Tumors from CT Images Using a Single Click Ensemble Segmentation Approach. PATTERN RECOGNITION 2013; 46:692-702. [PMID: 23459617 PMCID: PMC3580869 DOI: 10.1016/j.patcog.2012.10.005] [Citation(s) in RCA: 71] [Impact Index Per Article: 6.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/03/2023]
Abstract
A single click ensemble segmentation (SCES) approach based on an existing "Click&Grow" algorithm is presented. The SCES approach requires only one operator selected seed point as compared with multiple operator inputs, which are typically needed. This facilitates processing large numbers of cases. Evaluation on a set of 129 CT lung tumor images using a similarity index (SI) was done. The average SI is above 93% using 20 different start seeds, showing stability. The average SI for 2 different readers was 79.53%. We then compared the SCES algorithm with the two readers, the level set algorithm and the skeleton graph cut algorithm obtaining an average SI of 78.29%, 77.72%, 63.77% and 63.76% respectively. We can conclude that the newly developed automatic lung lesion segmentation algorithm is stable, accurate and automated.
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Affiliation(s)
- Yuhua Gu
- Department of Imaging, H. Lee Moffitt Cancer Center and Research Institute, Tampa, Florida 33612. USA
| | - Virendra Kumar
- Department of Imaging, H. Lee Moffitt Cancer Center and Research Institute, Tampa, Florida 33612. USA
| | - Lawrence O Hall
- Department of Computer Science and Engineering, University of South Florida, Tampa, Florida 33620. USA
| | - Dmitry B Goldgof
- Department of Computer Science and Engineering, University of South Florida, Tampa, Florida 33620. USA
| | - Ching-Yen Li
- Department of Computer Science and Engineering, University of South Florida, Tampa, Florida 33620. USA
| | - René Korn
- Definiens AG, Trappentreustraße 1, 80339 München, Germany
| | - Claus Bendtsen
- DECS, AstraZeneca, 50S27 Mereside, Alderley Park, Macclesfield, Cheshire SK10 4TG, UK
| | | | - Andre Dekker
- Departments of Radiation Oncology, University Hospital Maastricht, Maastricht, Netherlands
| | - Hugo Aerts
- Departments of Radiation Oncology, University Hospital Maastricht, Maastricht, Netherlands
| | - Philippe Lambin
- Departments of Radiation Oncology, University Hospital Maastricht, Maastricht, Netherlands
| | - Xiuli Li
- Medical Image Processing Group, State Key Laboratory of Management and Control for Complex Systems, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China
| | - Jie Tian
- Medical Image Processing Group, State Key Laboratory of Management and Control for Complex Systems, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China
| | - Robert A Gatenby
- Department of Imaging, H. Lee Moffitt Cancer Center and Research Institute, Tampa, Florida 33612. USA
| | - Robert J Gillies
- Department of Imaging, H. Lee Moffitt Cancer Center and Research Institute, Tampa, Florida 33612. USA
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Kumar V, Gu Y, Basu S, Berglund A, Eschrich SA, Schabath MB, Forster K, Aerts HJ, Dekker A, Fenstermacher D, Goldgof DB, Hall LO, Lambin P, Balagurunathan Y, Gatenby RA, Gillies RJ. Radiomics: the process and the challenges. Magn Reson Imaging 2012; 30:1234-48. [PMID: 22898692 PMCID: PMC3563280 DOI: 10.1016/j.mri.2012.06.010] [Citation(s) in RCA: 1418] [Impact Index Per Article: 118.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/23/2012] [Revised: 06/19/2012] [Accepted: 06/21/2012] [Indexed: 10/28/2022]
Abstract
"Radiomics" refers to the extraction and analysis of large amounts of advanced quantitative imaging features with high throughput from medical images obtained with computed tomography, positron emission tomography or magnetic resonance imaging. Importantly, these data are designed to be extracted from standard-of-care images, leading to a very large potential subject pool. Radiomics data are in a mineable form that can be used to build descriptive and predictive models relating image features to phenotypes or gene-protein signatures. The core hypothesis of radiomics is that these models, which can include biological or medical data, can provide valuable diagnostic, prognostic or predictive information. The radiomics enterprise can be divided into distinct processes, each with its own challenges that need to be overcome: (a) image acquisition and reconstruction, (b) image segmentation and rendering, (c) feature extraction and feature qualification and (d) databases and data sharing for eventual (e) ad hoc informatics analyses. Each of these individual processes poses unique challenges. For example, optimum protocols for image acquisition and reconstruction have to be identified and harmonized. Also, segmentations have to be robust and involve minimal operator input. Features have to be generated that robustly reflect the complexity of the individual volumes, but cannot be overly complex or redundant. Furthermore, informatics databases that allow incorporation of image features and image annotations, along with medical and genetic data, have to be generated. Finally, the statistical approaches to analyze these data have to be optimized, as radiomics is not a mature field of study. Each of these processes will be discussed in turn, as well as some of their unique challenges and proposed approaches to solve them. The focus of this article will be on images of non-small-cell lung cancer.
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Affiliation(s)
- Virendra Kumar
- Department of Cancer Imaging and Metabolism, H. Lee Moffitt Cancer Center and Research Institute, Tampa, Florida, USA
| | - Yuhua Gu
- Department of Cancer Imaging and Metabolism, H. Lee Moffitt Cancer Center and Research Institute, Tampa, Florida, USA
| | - Satrajit Basu
- Department of Computer Science and Engineering, University of South Florida, Tampa, Florida, USA
| | - Anders Berglund
- Department of Bioinformatics, H. Lee Moffitt Cancer Center and Research Institute, Tampa, Florida, USA
| | - Steven A. Eschrich
- Department of Bioinformatics, H. Lee Moffitt Cancer Center and Research Institute, Tampa, Florida, USA
| | - Matthew B. Schabath
- Department of Cancer Epidemiology, H. Lee Moffitt Cancer Center and Research Institute, Tampa, Florida, USA
| | - Kenneth Forster
- Department of Radiation Oncology, H. Lee Moffitt Cancer Center and Research Institute, Tampa, Florida, USA
| | - Hugo J.W.L. Aerts
- Department of Radiation Oncology (MAASTRO), GROW-School for Oncology and Developmental Biology, Maastricht University Medical Center, Maastricht, The Netherlands
- Computational Biology and Functional Genomics Laboratory, Department of Biostatistics and Computational Biology, Dana-Farber Cancer Institute, Harvard School of Public Health, Boston, Massachusetts, USA
| | - Andre Dekker
- Department of Radiation Oncology (MAASTRO), GROW-School for Oncology and Developmental Biology, Maastricht University Medical Center, Maastricht, The Netherlands
| | - David Fenstermacher
- Department of Bioinformatics, H. Lee Moffitt Cancer Center and Research Institute, Tampa, Florida, USA
| | - Dmitry B Goldgof
- Department of Computer Science and Engineering, University of South Florida, Tampa, Florida, USA
| | - Lawrence O Hall
- Department of Computer Science and Engineering, University of South Florida, Tampa, Florida, USA
| | - Philippe Lambin
- Department of Radiation Oncology (MAASTRO), GROW-School for Oncology and Developmental Biology, Maastricht University Medical Center, Maastricht, The Netherlands
| | - Yoganand Balagurunathan
- Department of Cancer Imaging and Metabolism, H. Lee Moffitt Cancer Center and Research Institute, Tampa, Florida, USA
| | - Robert A Gatenby
- Department of Radiology, H. Lee Moffitt Cancer Center and Research Institute, Tampa, Florida, USA
| | - Robert J Gillies
- Department of Cancer Imaging and Metabolism, H. Lee Moffitt Cancer Center and Research Institute, Tampa, Florida, USA
- Department of Radiology, H. Lee Moffitt Cancer Center and Research Institute, Tampa, Florida, USA
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He Q, Duan Y, Karsch K, Miles J. Detecting corpus callosum abnormalities in autism based on anatomical landmarks. Psychiatry Res 2010; 183:126-32. [PMID: 20620032 PMCID: PMC2910223 DOI: 10.1016/j.pscychresns.2010.05.006] [Citation(s) in RCA: 16] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/24/2009] [Revised: 04/05/2010] [Accepted: 05/16/2010] [Indexed: 10/19/2022]
Abstract
Autism is a severe developmental disorder whose neurological basis is largely unknown. The aim of this study was to identify the shape differences of the corpus callosum between patients with autism and control subjects. Anatomical landmarks were collected from midsagittal magnetic resonance images of 25 patients and 18 controls. Euclidean distance matrix analysis and thin-plate spline analyses were used to examine the landmark forms. Point-by-point shape comparison was performed both globally and locally. A new local shape comparison scheme was proposed which compared each part of the shape in its local coordinate system. Point correspondence was established among individual shapes based on the inherent landmark correspondence. No significant difference was found in the landmark form between patients and controls, but the distance between the interior genu and the posterior-most section was found to be significantly shorter in patients. Thin-plate spline analysis showed significant group differences between the landmark configurations in terms of the deformation from the overall mean configuration. Significant global shape differences were found in the anterior lower body and posterior bottom, and there was a local shape difference in the anterior bottom. This study can serve as both a clinical reference and a detailed procedural guideline for similar studies in the future.
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Affiliation(s)
- Qing He
- Department of Computer Science, University of Missouri-Columbia, Columbia, MO, 65211, USA.
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A multiscale model for virus capsid dynamics. Int J Biomed Imaging 2010; 2010:308627. [PMID: 20224756 PMCID: PMC2836135 DOI: 10.1155/2010/308627] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/25/2009] [Accepted: 11/25/2009] [Indexed: 11/18/2022] Open
Abstract
Viruses are infectious agents that can cause epidemics and pandemics. The understanding of virus formation, evolution, stability, and interaction with host cells is of great importance to the scientific community and public health. Typically, a virus complex in association with its aquatic environment poses a fabulous challenge to theoretical description and prediction. In this work, we propose a differential geometry-based multiscale paradigm to model complex biomolecule systems. In our approach, the differential geometry theory of surfaces and geometric measure theory are employed as a natural means to couple the macroscopic continuum domain of the fluid mechanical description of the aquatic environment from the microscopic discrete domain of the atomistic description of the biomolecule. A multiscale action functional is constructed as a unified framework to derive the governing equations for the dynamics of different scales. We show that the classical Navier-Stokes equation for the fluid dynamics and Newton's equation for the molecular dynamics can be derived from the least action principle. These equations are coupled through the continuum-discrete interface whose dynamics is governed by potential driven geometric flows.
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A Novel Algorithm for Automatic Brain Structure Segmentation from MRI. ADVANCES IN VISUAL COMPUTING 2008. [DOI: 10.1007/978-3-540-89639-5_53] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/28/2023]
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