Pub Date : 2010-04-14DOI: 10.1109/ISBI.2010.5490399
Zhaozheng Yin, Ryoma Bise, Mei Chen, T. Kanade
Cell segmentation in microscopy imagery is essential for many bioimage applications such as cell tracking. To segment cells from the background accurately, we present a pixel classification approach that is independent of cell type or imaging modality. We train a set of Bayesian classifiers from clustered local training image patches. Each Bayesian classifier is an expert to make decision in its specific domain. The decision from the mixture of experts determines how likely a new pixel is a cell pixel. We demonstrate the effectiveness of this approach on four cell types with diverse morphologies under different microscopy imaging modalities.
{"title":"Cell segmentation in microscopy imagery using a bag of local Bayesian classifiers","authors":"Zhaozheng Yin, Ryoma Bise, Mei Chen, T. Kanade","doi":"10.1109/ISBI.2010.5490399","DOIUrl":"https://doi.org/10.1109/ISBI.2010.5490399","url":null,"abstract":"Cell segmentation in microscopy imagery is essential for many bioimage applications such as cell tracking. To segment cells from the background accurately, we present a pixel classification approach that is independent of cell type or imaging modality. We train a set of Bayesian classifiers from clustered local training image patches. Each Bayesian classifier is an expert to make decision in its specific domain. The decision from the mixture of experts determines how likely a new pixel is a cell pixel. We demonstrate the effectiveness of this approach on four cell types with diverse morphologies under different microscopy imaging modalities.","PeriodicalId":250523,"journal":{"name":"2010 IEEE International Symposium on Biomedical Imaging: From Nano to Macro","volume":"37 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2010-04-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127748850","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2010-04-14DOI: 10.1109/ISBI.2010.5490351
A. Gasnier, R. Ardon, C. Ciofolo-Veit, E. Leen, J. Correas
3D contrast-enhanced ultrasound (CEUS) is a powerful imaging technique for tumour vascularity assessment, which is critical for radio-frequency ablation (RFA) planning or for the assessment of response to antiangiogenic therapies. In this paper, we propose a novel semi-automated method for the quantification of tumour vascularity in 3D CEUS data. We apply a two-step framework combining an interactive segmentation of the tumour necrosis followed by an automatic detection of the vascularity based on implicit representations. Experimental results on 3D CEUS images of renal cell carcinomas (RCC) show that our method is promising in terms of speed and quality.
{"title":"Assessing tumour vascularity with 3D contrast-enhanced ultrasound: A new semi-automated segmentation framework","authors":"A. Gasnier, R. Ardon, C. Ciofolo-Veit, E. Leen, J. Correas","doi":"10.1109/ISBI.2010.5490351","DOIUrl":"https://doi.org/10.1109/ISBI.2010.5490351","url":null,"abstract":"3D contrast-enhanced ultrasound (CEUS) is a powerful imaging technique for tumour vascularity assessment, which is critical for radio-frequency ablation (RFA) planning or for the assessment of response to antiangiogenic therapies. In this paper, we propose a novel semi-automated method for the quantification of tumour vascularity in 3D CEUS data. We apply a two-step framework combining an interactive segmentation of the tumour necrosis followed by an automatic detection of the vascularity based on implicit representations. Experimental results on 3D CEUS images of renal cell carcinomas (RCC) show that our method is promising in terms of speed and quality.","PeriodicalId":250523,"journal":{"name":"2010 IEEE International Symposium on Biomedical Imaging: From Nano to Macro","volume":"114 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2010-04-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132751118","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2010-04-14DOI: 10.1109/ISBI.2010.5490141
D. Sussman, Jianhua Yao, R. Summers
Obesity has become widespread in America and has been identified as a risk factor for many illnesses. Measuring adipose tissue (AT) with traditional means is often unreliable and inaccurate. MRI provides a safe and minimally invasive means to measure AT accurately and segment visceral AT from subcutaneous AT. However, MRI is often corrupted by image artifacts which make manual measurements difficult and time consuming. We present a fully automated method to measure and segment abdominal AT in MRI. Our method uses non-parametric non-uniform intensity normalization (N3) to correct for image artifacts and inhomogeneities, fuzzy c-means to cluster AT regions and active contour models to separate subcutaneous and visceral AT. Our method was able to measure images with severe intensity inhomogeneities and demonstrated agreement with two manual users that was close to the agreement between the manual users.
{"title":"Automated measurement and segmentation of abdominal adipose tissue in MRI","authors":"D. Sussman, Jianhua Yao, R. Summers","doi":"10.1109/ISBI.2010.5490141","DOIUrl":"https://doi.org/10.1109/ISBI.2010.5490141","url":null,"abstract":"Obesity has become widespread in America and has been identified as a risk factor for many illnesses. Measuring adipose tissue (AT) with traditional means is often unreliable and inaccurate. MRI provides a safe and minimally invasive means to measure AT accurately and segment visceral AT from subcutaneous AT. However, MRI is often corrupted by image artifacts which make manual measurements difficult and time consuming. We present a fully automated method to measure and segment abdominal AT in MRI. Our method uses non-parametric non-uniform intensity normalization (N3) to correct for image artifacts and inhomogeneities, fuzzy c-means to cluster AT regions and active contour models to separate subcutaneous and visceral AT. Our method was able to measure images with severe intensity inhomogeneities and demonstrated agreement with two manual users that was close to the agreement between the manual users.","PeriodicalId":250523,"journal":{"name":"2010 IEEE International Symposium on Biomedical Imaging: From Nano to Macro","volume":"13 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2010-04-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133278747","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2010-04-14DOI: 10.1109/ISBI.2010.5490300
C. Constantinides, N. Aristokleous, G. Johnson, Dimitris Perperides
Magnetic Resonance Imaging (MRI) has exhibited significant potential for quantifying cardiac function and dysfunction in the mouse. Recent advances in high-resolution cardiac MR imaging techniques have contributed to the development of acquisition approaches that allow fast and accurate description of anatomic structures, and accurate surface and finite element (FE) mesh model constructions for study of global mechanical function in normal and transgenic mice. This study presents work in progress for construction of quantitatively accurate three-dimensional (3D) and 4D dynamic surface and FE models of murine left ventricular (LV) muscle in C57BL/6J (n=10) mice. Constructed models are subsequently imported into commercial software packages for the solution of the constitutive equations that characterize mechanical function, including computation of the stress and strain fields. They are further used with solid-free form fabrication processes to construct model-based material renditions of the human and mouse hearts.
{"title":"Static and dynamic cardiac modelling: Initial strides and results towards a quantitatively accurate mechanical heart model","authors":"C. Constantinides, N. Aristokleous, G. Johnson, Dimitris Perperides","doi":"10.1109/ISBI.2010.5490300","DOIUrl":"https://doi.org/10.1109/ISBI.2010.5490300","url":null,"abstract":"Magnetic Resonance Imaging (MRI) has exhibited significant potential for quantifying cardiac function and dysfunction in the mouse. Recent advances in high-resolution cardiac MR imaging techniques have contributed to the development of acquisition approaches that allow fast and accurate description of anatomic structures, and accurate surface and finite element (FE) mesh model constructions for study of global mechanical function in normal and transgenic mice. This study presents work in progress for construction of quantitatively accurate three-dimensional (3D) and 4D dynamic surface and FE models of murine left ventricular (LV) muscle in C57BL/6J (n=10) mice. Constructed models are subsequently imported into commercial software packages for the solution of the constitutive equations that characterize mechanical function, including computation of the stress and strain fields. They are further used with solid-free form fabrication processes to construct model-based material renditions of the human and mouse hearts.","PeriodicalId":250523,"journal":{"name":"2010 IEEE International Symposium on Biomedical Imaging: From Nano to Macro","volume":"16 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2010-04-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131929169","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2010-04-14DOI: 10.1109/ISBI.2010.5490188
F. Deligianni, E. Robinson, C. Beckmann, D. Sharp, A. Edwards, D. Rueckert
Studies that examine the relationship of functional and structural connectivity are tremendously important in interpreting neurophysiological data. Although, the relationship between functional and structural connectivity has been explored with a number of statistical tools [1, 2], there is no explicit attempt to quantitatively measure how well functional data can be predicted from structural data. Here, we predict functional connectivity from structural connectivity, explicitly, by utilizing a predictive model based on PCA and CCA. The combination of these techniques allowed the reduction of dimensionality and modeling of inter-correlations, successfully. We provide both qualitative and quantitative results based on a leave-one-out validation.
{"title":"Inference of functional connectivity from structural brain connectivity","authors":"F. Deligianni, E. Robinson, C. Beckmann, D. Sharp, A. Edwards, D. Rueckert","doi":"10.1109/ISBI.2010.5490188","DOIUrl":"https://doi.org/10.1109/ISBI.2010.5490188","url":null,"abstract":"Studies that examine the relationship of functional and structural connectivity are tremendously important in interpreting neurophysiological data. Although, the relationship between functional and structural connectivity has been explored with a number of statistical tools [1, 2], there is no explicit attempt to quantitatively measure how well functional data can be predicted from structural data. Here, we predict functional connectivity from structural connectivity, explicitly, by utilizing a predictive model based on PCA and CCA. The combination of these techniques allowed the reduction of dimensionality and modeling of inter-correlations, successfully. We provide both qualitative and quantitative results based on a leave-one-out validation.","PeriodicalId":250523,"journal":{"name":"2010 IEEE International Symposium on Biomedical Imaging: From Nano to Macro","volume":"8 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2010-04-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134100696","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2010-04-14DOI: 10.1109/ISBI.2010.5490189
Joanna Klukowska, G. Herman, I. Kazantsev
We propose a method of correction for distance-dependent blurring, which is one of the limiting factors to achieving higher resolution in 3D reconstructions of biological specimens from 2D projections obtained by an electron microscope. Our proposed correction is based on the frequency-distance relation that has been used successfully in correction of a similar problem in single photon emission tomography and has been suggested for electron microscopy data obtained by rotating a sample around a single axis. We extend these approaches to electron microscopy data that are obtained from arbitrary directions. We develop the theoretical background for a correction method that results in an estimate of a true projection data set, which then can be used to obtain a 3D reconstruction by any currently existing algorithm.
{"title":"Correction of distance-dependent blurring in projection data for fully three-dimensional electron microscopic reconstruction","authors":"Joanna Klukowska, G. Herman, I. Kazantsev","doi":"10.1109/ISBI.2010.5490189","DOIUrl":"https://doi.org/10.1109/ISBI.2010.5490189","url":null,"abstract":"We propose a method of correction for distance-dependent blurring, which is one of the limiting factors to achieving higher resolution in 3D reconstructions of biological specimens from 2D projections obtained by an electron microscope. Our proposed correction is based on the frequency-distance relation that has been used successfully in correction of a similar problem in single photon emission tomography and has been suggested for electron microscopy data obtained by rotating a sample around a single axis. We extend these approaches to electron microscopy data that are obtained from arbitrary directions. We develop the theoretical background for a correction method that results in an estimate of a true projection data set, which then can be used to obtain a 3D reconstruction by any currently existing algorithm.","PeriodicalId":250523,"journal":{"name":"2010 IEEE International Symposium on Biomedical Imaging: From Nano to Macro","volume":"57 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2010-04-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115238150","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2010-04-14DOI: 10.1109/ISBI.2010.5490261
P. Coupé, P. Hellier, X. Morandi, C. Barillot
In this paper, a global approach based on 3D freehand ultrasound imaging is proposed to (a) correct the error of the neuronavigation system in image-patient registration and (b) compensate for the deformations of the cerebral structures occurring during a neurosurgical procedure. The rigid and non rigid multimodal registrations are achieved by matching the hyperechogenic structures of brain. The quantitative evaluation of the non rigid registration was performed within a framework based on synthetic deformation. Finally, experiments were carried out on real data sets of 4 patients with lesions such as cavernoma and low-grade glioma. Qualitative and quantitative results on the estimated error performed by neuronavigation system and the estimated brain deformations are given.
{"title":"Intraoperative ultrasonography for the correction of brainshift based on the matching of hyperechogenic structures","authors":"P. Coupé, P. Hellier, X. Morandi, C. Barillot","doi":"10.1109/ISBI.2010.5490261","DOIUrl":"https://doi.org/10.1109/ISBI.2010.5490261","url":null,"abstract":"In this paper, a global approach based on 3D freehand ultrasound imaging is proposed to (a) correct the error of the neuronavigation system in image-patient registration and (b) compensate for the deformations of the cerebral structures occurring during a neurosurgical procedure. The rigid and non rigid multimodal registrations are achieved by matching the hyperechogenic structures of brain. The quantitative evaluation of the non rigid registration was performed within a framework based on synthetic deformation. Finally, experiments were carried out on real data sets of 4 patients with lesions such as cavernoma and low-grade glioma. Qualitative and quantitative results on the estimated error performed by neuronavigation system and the estimated brain deformations are given.","PeriodicalId":250523,"journal":{"name":"2010 IEEE International Symposium on Biomedical Imaging: From Nano to Macro","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2010-04-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114538345","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2010-04-14DOI: 10.1109/ISBI.2010.5490308
G. Gao, Phani Chinchapatnam, M. Wright, A. Arujuna, M. Ginks, C. Rinaldi, K. Rhode
In this paper we are proposing an MRI/CT-guided cardiac electroanatomical mapping system, EpreMap. EpreMap was developed as an extension to the guidance system developed at King's College London for cardiac electrophysiology procedures. This platform allows for both the registration of MRI/CT anatomical data to X-ray fluoroscopy and the determination of the catheter positions. EpreMap is a contact mapping system. By using a radial basis function (RBF) based scattered data interpolation algorithm, EpreMap can create a cardiac activation map for a whole chamber starting from five sampling points. The activation map is updated with every new measurement. Offline studies showed that cardiac activation maps created by using EpreMap were highly correlated with the maps created by using a non-contact mapping system. EpreMap was used in three clinical cases. The clinical studies proved the workflow of EpreMap was valid in the clinical environment. For one clinical case, the result of EpreMap was validated against a non-contact mapping system. Clinically significant regions identified by using the two mapping systems were strongly correlated.
{"title":"An MRI/CT-based cardiac electroanatomical mapping system with scattered data interpolation algorithm","authors":"G. Gao, Phani Chinchapatnam, M. Wright, A. Arujuna, M. Ginks, C. Rinaldi, K. Rhode","doi":"10.1109/ISBI.2010.5490308","DOIUrl":"https://doi.org/10.1109/ISBI.2010.5490308","url":null,"abstract":"In this paper we are proposing an MRI/CT-guided cardiac electroanatomical mapping system, EpreMap. EpreMap was developed as an extension to the guidance system developed at King's College London for cardiac electrophysiology procedures. This platform allows for both the registration of MRI/CT anatomical data to X-ray fluoroscopy and the determination of the catheter positions. EpreMap is a contact mapping system. By using a radial basis function (RBF) based scattered data interpolation algorithm, EpreMap can create a cardiac activation map for a whole chamber starting from five sampling points. The activation map is updated with every new measurement. Offline studies showed that cardiac activation maps created by using EpreMap were highly correlated with the maps created by using a non-contact mapping system. EpreMap was used in three clinical cases. The clinical studies proved the workflow of EpreMap was valid in the clinical environment. For one clinical case, the result of EpreMap was validated against a non-contact mapping system. Clinically significant regions identified by using the two mapping systems were strongly correlated.","PeriodicalId":250523,"journal":{"name":"2010 IEEE International Symposium on Biomedical Imaging: From Nano to Macro","volume":"171 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2010-04-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114765082","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2010-04-14DOI: 10.1109/ISBI.2010.5490324
Yangming Ou, A. Besbes, M. Bilello, Mohamed Mansour, C. Davatzikos, N. Paragios
In this paper, we present a framework for extracting mutually-salient landmark pairs for registration. Traditional methods detect landmarks one-by-one and separately in two images. Therefore, the detected landmarks might inherit low dis-criminability and are not necessarily good for matching. In contrast, our method detects landmarks pair-by-pair across images, and those pairs are required to be mutually-salient, i.e., uniquely corresponding to each other. The second merit of our framework is that, instead of finding individually optimal correspondence, which is a local approach and could cause self-intersection of the resultant deformation, our framework adopts a Markov-random-field (MRF)-based spatial arrangement to select the globally optimal landmark pairs. In this way, the geometric consistency of the correspondences is maintained and the resultant deformations are relatively smooth and topology-preserving. Promising experimental validation through a radiologist's evaluation of the established correspondences is presented.
{"title":"Detecting mutually-salient landmark pairs with MRF regularization","authors":"Yangming Ou, A. Besbes, M. Bilello, Mohamed Mansour, C. Davatzikos, N. Paragios","doi":"10.1109/ISBI.2010.5490324","DOIUrl":"https://doi.org/10.1109/ISBI.2010.5490324","url":null,"abstract":"In this paper, we present a framework for extracting mutually-salient landmark pairs for registration. Traditional methods detect landmarks one-by-one and separately in two images. Therefore, the detected landmarks might inherit low dis-criminability and are not necessarily good for matching. In contrast, our method detects landmarks pair-by-pair across images, and those pairs are required to be mutually-salient, i.e., uniquely corresponding to each other. The second merit of our framework is that, instead of finding individually optimal correspondence, which is a local approach and could cause self-intersection of the resultant deformation, our framework adopts a Markov-random-field (MRF)-based spatial arrangement to select the globally optimal landmark pairs. In this way, the geometric consistency of the correspondences is maintained and the resultant deformations are relatively smooth and topology-preserving. Promising experimental validation through a radiologist's evaluation of the established correspondences is presented.","PeriodicalId":250523,"journal":{"name":"2010 IEEE International Symposium on Biomedical Imaging: From Nano to Macro","volume":"49 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2010-04-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"117313069","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2010-04-14DOI: 10.1109/ISBI.2010.5490207
S. Sotiropoulos, David E. Jones, L. Bai, T. Kypraios
Characterizing the fibre orientation uncertainty is essential for quantitative tractography approaches, such as probabilistic tracking. We present an analytic way to perform Bayesian inference on diffusion ODFs from Q-ball imaging data. Drawing a random sample of ODFs reduces to sampling a multivariate t distribution. Assuming that the local ODF maxima provide fibre orientations, a random sample of orientations can then be directly obtained from the ODF sample. Contrary to approximate inference approaches, such as MCMC, our method samples from the exact posterior distribution. Results are illustrated on simulated and human in-vivo data.
{"title":"Exact and analytic bayesian inference for orientation distribution functions","authors":"S. Sotiropoulos, David E. Jones, L. Bai, T. Kypraios","doi":"10.1109/ISBI.2010.5490207","DOIUrl":"https://doi.org/10.1109/ISBI.2010.5490207","url":null,"abstract":"Characterizing the fibre orientation uncertainty is essential for quantitative tractography approaches, such as probabilistic tracking. We present an analytic way to perform Bayesian inference on diffusion ODFs from Q-ball imaging data. Drawing a random sample of ODFs reduces to sampling a multivariate t distribution. Assuming that the local ODF maxima provide fibre orientations, a random sample of orientations can then be directly obtained from the ODF sample. Contrary to approximate inference approaches, such as MCMC, our method samples from the exact posterior distribution. Results are illustrated on simulated and human in-vivo data.","PeriodicalId":250523,"journal":{"name":"2010 IEEE International Symposium on Biomedical Imaging: From Nano to Macro","volume":"13 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2010-04-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123164211","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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