Pub Date : 2008-05-14DOI: 10.1109/ISBI.2008.4541231
D. Afonso, J. Sanches, M. Lauterbach
The emerging functional MRI (magnetic resonance imaging), fMRI, imaging modality was developed to obtain non-invasive information regarding the neural processes behind pre-determined task. The data is gathered in such a way that the extraction certainty of the desired information is maximized. Still this is a difficult task due to low Signal-to-Noise Ratio (SNR), corrupting noise and artifacts from several sources. The most prevalent method, here called SPM-GLM uses a conventional statistical inference methodology based on the t-statistics, where it assumes a rather rigid shape on the BOLD hemodynamic response function (HRF), constant for the whole region of interest (ROI). A new algorithm, designed in a Bayesian framework, is presented in this paper, called SPM-MAP. The algorithm jointly detects the brain activated regions and the underlying HRF in an adaptative and local basis. This approach presents two main advantages: (1) the activity detection benefits from the method's high flexibility toward the HRF shape; (2) it provides local estimations for the HRF. The SPM-MAP algorithm is validated by using Monte Carlo tests with synthetic data and comparisons with the SPM-GLM are also performed. Tests using real data are also performed and results are compared with the ones provided by the SPM-GLM method tuned by the medical doctor.
{"title":"FMRI brain activity and underlying hemodynamics estimation in a new Bayesian framework","authors":"D. Afonso, J. Sanches, M. Lauterbach","doi":"10.1109/ISBI.2008.4541231","DOIUrl":"https://doi.org/10.1109/ISBI.2008.4541231","url":null,"abstract":"The emerging functional MRI (magnetic resonance imaging), fMRI, imaging modality was developed to obtain non-invasive information regarding the neural processes behind pre-determined task. The data is gathered in such a way that the extraction certainty of the desired information is maximized. Still this is a difficult task due to low Signal-to-Noise Ratio (SNR), corrupting noise and artifacts from several sources. The most prevalent method, here called SPM-GLM uses a conventional statistical inference methodology based on the t-statistics, where it assumes a rather rigid shape on the BOLD hemodynamic response function (HRF), constant for the whole region of interest (ROI). A new algorithm, designed in a Bayesian framework, is presented in this paper, called SPM-MAP. The algorithm jointly detects the brain activated regions and the underlying HRF in an adaptative and local basis. This approach presents two main advantages: (1) the activity detection benefits from the method's high flexibility toward the HRF shape; (2) it provides local estimations for the HRF. The SPM-MAP algorithm is validated by using Monte Carlo tests with synthetic data and comparisons with the SPM-GLM are also performed. Tests using real data are also performed and results are compared with the ones provided by the SPM-GLM method tuned by the medical doctor.","PeriodicalId":184204,"journal":{"name":"2008 5th IEEE International Symposium on Biomedical Imaging: From Nano to Macro","volume":"65 4 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2008-05-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129600628","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 : 2008-05-14DOI: 10.1109/ISBI.2008.4541211
R. Narayanan, D. Shen, C. Davatzikos, E. Crawford, A. Barqawi, P. Werahera, D. Kumar, J. Suri
It is widely established that prostate cancer is a multifocal disease and cancerous lesions are not uniformly distributed within the gland. Current imaging methods cannot detect prostate cancer with sufficient sensitivity and specificity, especially localized cancers. A cancer atlas was previously demonstrated. However the atlas must be registered with a patient's ultrasound image in a clinical procedure. Here we present the fast registration of this atlas in a clinical setting so as to map cancer likelihoods in addition to optimized biopsy locations from the atlas space to the subject to maximize cancer detection accuracy. The registration was validated on 158 subjects with cancers annotated and the detection rate was found to be 84.81% and 89.87% for optimized 7 and 12 core biopsy schemes respectively. It took less than 8 seconds for the entire registration procedure.
{"title":"Application and validation of registration framework for real-time atlas guided biopsy","authors":"R. Narayanan, D. Shen, C. Davatzikos, E. Crawford, A. Barqawi, P. Werahera, D. Kumar, J. Suri","doi":"10.1109/ISBI.2008.4541211","DOIUrl":"https://doi.org/10.1109/ISBI.2008.4541211","url":null,"abstract":"It is widely established that prostate cancer is a multifocal disease and cancerous lesions are not uniformly distributed within the gland. Current imaging methods cannot detect prostate cancer with sufficient sensitivity and specificity, especially localized cancers. A cancer atlas was previously demonstrated. However the atlas must be registered with a patient's ultrasound image in a clinical procedure. Here we present the fast registration of this atlas in a clinical setting so as to map cancer likelihoods in addition to optimized biopsy locations from the atlas space to the subject to maximize cancer detection accuracy. The registration was validated on 158 subjects with cancers annotated and the detection rate was found to be 84.81% and 89.87% for optimized 7 and 12 core biopsy schemes respectively. It took less than 8 seconds for the entire registration procedure.","PeriodicalId":184204,"journal":{"name":"2008 5th IEEE International Symposium on Biomedical Imaging: From Nano to Macro","volume":"25 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2008-05-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130953256","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 : 2008-05-14DOI: 10.1109/ISBI.2008.4540995
C. Ortíz-de-Solórzano, T. Pengo, Miguel Galarraga, A. Muñoz-Barrutia
We present the hardware and software specification of a quantitative, multidimensional and multispectral microscopy system designed for the detection of lung cancer using minimal samples of bronchoalveolar lavage (BAL). BAL samples were stained using FICTION: Fluorescence Immunophenotyping and Interphase Cytogenetics as a Tool for the Investigation of Neoplasms. Our system allows preliminary immunophenotypic detection of rare cancerous candidate cells, followed by accurate three-dimensional analysis of genomic integrity, to confirm or refute the initial assessment. Our results show that our automated analysis can accurately assist a human expert in the diagnostic evaluation of BAL samples.
{"title":"Automation of the detection of lung cancer cells in minimal samples of bronchioalveolar lavage","authors":"C. Ortíz-de-Solórzano, T. Pengo, Miguel Galarraga, A. Muñoz-Barrutia","doi":"10.1109/ISBI.2008.4540995","DOIUrl":"https://doi.org/10.1109/ISBI.2008.4540995","url":null,"abstract":"We present the hardware and software specification of a quantitative, multidimensional and multispectral microscopy system designed for the detection of lung cancer using minimal samples of bronchoalveolar lavage (BAL). BAL samples were stained using FICTION: Fluorescence Immunophenotyping and Interphase Cytogenetics as a Tool for the Investigation of Neoplasms. Our system allows preliminary immunophenotypic detection of rare cancerous candidate cells, followed by accurate three-dimensional analysis of genomic integrity, to confirm or refute the initial assessment. Our results show that our automated analysis can accurately assist a human expert in the diagnostic evaluation of BAL samples.","PeriodicalId":184204,"journal":{"name":"2008 5th IEEE International Symposium on Biomedical Imaging: From Nano to Macro","volume":"123 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2008-05-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131202952","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 : 2008-05-14DOI: 10.1109/ISBI.2008.4541261
H. Liao, G. Sapiro
In limited data tomography, with applications such as electron microscopy and medical imaging, the scanning views are within an angular range that is often both limited and sparsely sampled. In these situations, standard algorithms produce reconstructions with notorious artifacts. We show in this paper that a sparsity image representation principle, based on learning dictionaries for sparse representations of image patches, leads to significantly improved reconstructions of the unknown density from its limited angle projections. The presentation of the underlying framework is complemented with illustrative results on artificial and real data.
{"title":"Sparse representations for limited data tomography","authors":"H. Liao, G. Sapiro","doi":"10.1109/ISBI.2008.4541261","DOIUrl":"https://doi.org/10.1109/ISBI.2008.4541261","url":null,"abstract":"In limited data tomography, with applications such as electron microscopy and medical imaging, the scanning views are within an angular range that is often both limited and sparsely sampled. In these situations, standard algorithms produce reconstructions with notorious artifacts. We show in this paper that a sparsity image representation principle, based on learning dictionaries for sparse representations of image patches, leads to significantly improved reconstructions of the unknown density from its limited angle projections. The presentation of the underlying framework is complemented with illustrative results on artificial and real data.","PeriodicalId":184204,"journal":{"name":"2008 5th IEEE International Symposium on Biomedical Imaging: From Nano to Macro","volume":"17 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2008-05-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127664424","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 : 2008-05-14DOI: 10.1109/ISBI.2008.4541086
P. Orlowski, J. Noble, Y. Ventikos, J. Byrne, P. Summers
A novel image-based patient-specific simulation method has been developed incorporating computational fluid dynamics (CFD) and porous media principles which presents, for the first time, patient-specific blood flow through an arteriovenous malformation of the brain (BAVM). The new approach constructs an image-based geometric model of a malformation where the BAVM nidus is modelled as a porous medium. The method has been applied to a brain BAVM case with two feeding and four draining vessels. A qualitative comparison of the simulation results with blood flow imaging data shows the promise of the approach and suggests that the method may find application in planning for BAVM treatment.
{"title":"Image-based simulation of brain arteriovenous malformation hemodynamics","authors":"P. Orlowski, J. Noble, Y. Ventikos, J. Byrne, P. Summers","doi":"10.1109/ISBI.2008.4541086","DOIUrl":"https://doi.org/10.1109/ISBI.2008.4541086","url":null,"abstract":"A novel image-based patient-specific simulation method has been developed incorporating computational fluid dynamics (CFD) and porous media principles which presents, for the first time, patient-specific blood flow through an arteriovenous malformation of the brain (BAVM). The new approach constructs an image-based geometric model of a malformation where the BAVM nidus is modelled as a porous medium. The method has been applied to a brain BAVM case with two feeding and four draining vessels. A qualitative comparison of the simulation results with blood flow imaging data shows the promise of the approach and suggests that the method may find application in planning for BAVM treatment.","PeriodicalId":184204,"journal":{"name":"2008 5th IEEE International Symposium on Biomedical Imaging: From Nano to Macro","volume":"74 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2008-05-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121037373","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 : 2008-05-14DOI: 10.1109/ISBI.2008.4541191
K. Passera, L. Mainardi, D. McGrath, J. Naish, D. Buckley, S. Cheung, Y. Watson, A. Caunce, G. Buonaccorsi, J. Logue, Marcus B. Taylor, C. Taylor, J. Waterton, H. Young, G. Parker
The objective of this work is to examine the feasibility of a method to register dynamic contrast enhanced computed X-ray tomography (DCE-CT) and dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) datasets in order to make possible the comparison of parametric maps generated from tracer kinetic modeling. First, the CT and MR dynamic sets were matched temporally using a cross-correlation maximization approach. The registration was then performed through an affine transformation followed by a non-linear registration using free-form deformations (FFDs) based on B-splines. This was determined from the CT-MR pair that maximized Normalized Mutual Information (NMI). Then the 'extended Kety' model was fitted to both CT and MR and Ktrans, Ve and vp parameters were obtained. The method was applied to 5 patients with bladder tumors. After registration, the overlap matching between CT and MR volume of interest (VOI) was on average 91%.
{"title":"A non-linear registration method for DCE-MRI and DCE-CT comparison in bladder tumors","authors":"K. Passera, L. Mainardi, D. McGrath, J. Naish, D. Buckley, S. Cheung, Y. Watson, A. Caunce, G. Buonaccorsi, J. Logue, Marcus B. Taylor, C. Taylor, J. Waterton, H. Young, G. Parker","doi":"10.1109/ISBI.2008.4541191","DOIUrl":"https://doi.org/10.1109/ISBI.2008.4541191","url":null,"abstract":"The objective of this work is to examine the feasibility of a method to register dynamic contrast enhanced computed X-ray tomography (DCE-CT) and dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) datasets in order to make possible the comparison of parametric maps generated from tracer kinetic modeling. First, the CT and MR dynamic sets were matched temporally using a cross-correlation maximization approach. The registration was then performed through an affine transformation followed by a non-linear registration using free-form deformations (FFDs) based on B-splines. This was determined from the CT-MR pair that maximized Normalized Mutual Information (NMI). Then the 'extended Kety' model was fitted to both CT and MR and Ktrans, Ve and vp parameters were obtained. The method was applied to 5 patients with bladder tumors. After registration, the overlap matching between CT and MR volume of interest (VOI) was on average 91%.","PeriodicalId":184204,"journal":{"name":"2008 5th IEEE International Symposium on Biomedical Imaging: From Nano to Macro","volume":"42 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2008-05-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127100477","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 : 2008-05-14DOI: 10.1109/ISBI.2008.4541070
A. Tucholka, B. Thirion, P. Pinel, J. Poline, J. F. Mangin
Defining precisely the position of active regions obtained from functional neuroimaging studies is challenging due to the functional and anatomical variability across subjects. Traditional volumetric normalization techniques ignore the geometry of the cortex and use a relatively imprecise three-dimensional coordinate system. In this study we propose an alternative method that relates the position of functional regions on the cortical surface to the positions of the main macro-anatomical structures, the sulci. Our approach consists of using the nearest sulci to build a local referential in which the position of a region is defined. This triangulation approach improves the localization of brain regions involved in various cognitive tasks.
{"title":"Triangulating cortical functional networks with anatomical landmarks","authors":"A. Tucholka, B. Thirion, P. Pinel, J. Poline, J. F. Mangin","doi":"10.1109/ISBI.2008.4541070","DOIUrl":"https://doi.org/10.1109/ISBI.2008.4541070","url":null,"abstract":"Defining precisely the position of active regions obtained from functional neuroimaging studies is challenging due to the functional and anatomical variability across subjects. Traditional volumetric normalization techniques ignore the geometry of the cortex and use a relatively imprecise three-dimensional coordinate system. In this study we propose an alternative method that relates the position of functional regions on the cortical surface to the positions of the main macro-anatomical structures, the sulci. Our approach consists of using the nearest sulci to build a local referential in which the position of a region is defined. This triangulation approach improves the localization of brain regions involved in various cognitive tasks.","PeriodicalId":184204,"journal":{"name":"2008 5th IEEE International Symposium on Biomedical Imaging: From Nano to Macro","volume":"34 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2008-05-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126072344","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 : 2008-05-14DOI: 10.1109/ISBI.2008.4541017
C. Matteau-Pelletier, M. Dehaes, F. Lesage, J. Lina
In this work we extend a previously proposed method for functional Magnetic Resonance Imaging (fMRI) to estimate the parameters of a linear model of diffuse optical imaging (DOI) time series. The regression is performed in the wavelet domain to infer drift coefficients at different scales and to estimate the strength of the hemodynamic response function (HRF). This multiresolution approach benefits from the whitening property of the Discrete Wavelet Transform (DWT), and we observe an estimation improvement which is then related to a quantitative measure of 1/f noise. The performances of the method are evaluated against a standard spline-cosine GLM approach with simulated HRF and real background physiology.
{"title":"Wavelet-based estimation of long-memory noise in Diffuse Optical Imaging","authors":"C. Matteau-Pelletier, M. Dehaes, F. Lesage, J. Lina","doi":"10.1109/ISBI.2008.4541017","DOIUrl":"https://doi.org/10.1109/ISBI.2008.4541017","url":null,"abstract":"In this work we extend a previously proposed method for functional Magnetic Resonance Imaging (fMRI) to estimate the parameters of a linear model of diffuse optical imaging (DOI) time series. The regression is performed in the wavelet domain to infer drift coefficients at different scales and to estimate the strength of the hemodynamic response function (HRF). This multiresolution approach benefits from the whitening property of the Discrete Wavelet Transform (DWT), and we observe an estimation improvement which is then related to a quantitative measure of 1/f noise. The performances of the method are evaluated against a standard spline-cosine GLM approach with simulated HRF and real background physiology.","PeriodicalId":184204,"journal":{"name":"2008 5th IEEE International Symposium on Biomedical Imaging: From Nano to Macro","volume":"57 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2008-05-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126766459","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 : 2008-05-14DOI: 10.1109/ISBI.2008.4541295
Leonid Teverovskiy, J. Becker, O. Lopez, Yanxi Liu
We propose a quantified asymmetry based method for age estimation. Our method uses machine learning to discover automatically the most discriminative asymmetry feature set from different brain regions and image scales. Applying this regression model on a Tl MR brain image set of 246 healthy individuals (121 females; 125 males, 66 plusmn 7.5 years old), we achieve a mean absolute error of 5.4 years and a mean signed error of -0.2 years for age estimation on unseen MR images using the stringent leave-15%-out cross validation. Our results show significant changes in asymmetry with aging in the following regions: the posterior horns of the lateral ventricles, the amygdala, the ventral putamen with a nearby region of the anterior inferior caudate nucleus, the basal fore- brain, hyppocampus and parahyppocampal regions. We confirm the validity of the age estimation model using permutation test on 30 replicas of the original dataset with randomly permuted ages (with p-value < 0.001). Furthermore, we apply this model to a separate set of MR images containing normal, Alzheimer's disease (AD) and mild cognitive impairment (MCI) subjects. Our results reflect the relative severity of brain pathology between the three subject groups: mean signed age estimation error is 0.6 years for normal controls, 2.2 years for MCI patients, and 4.7 years for AD patients.
{"title":"Quantified brain asymmetry for age estimation of normal and AD/MCI subjects","authors":"Leonid Teverovskiy, J. Becker, O. Lopez, Yanxi Liu","doi":"10.1109/ISBI.2008.4541295","DOIUrl":"https://doi.org/10.1109/ISBI.2008.4541295","url":null,"abstract":"We propose a quantified asymmetry based method for age estimation. Our method uses machine learning to discover automatically the most discriminative asymmetry feature set from different brain regions and image scales. Applying this regression model on a Tl MR brain image set of 246 healthy individuals (121 females; 125 males, 66 plusmn 7.5 years old), we achieve a mean absolute error of 5.4 years and a mean signed error of -0.2 years for age estimation on unseen MR images using the stringent leave-15%-out cross validation. Our results show significant changes in asymmetry with aging in the following regions: the posterior horns of the lateral ventricles, the amygdala, the ventral putamen with a nearby region of the anterior inferior caudate nucleus, the basal fore- brain, hyppocampus and parahyppocampal regions. We confirm the validity of the age estimation model using permutation test on 30 replicas of the original dataset with randomly permuted ages (with p-value < 0.001). Furthermore, we apply this model to a separate set of MR images containing normal, Alzheimer's disease (AD) and mild cognitive impairment (MCI) subjects. Our results reflect the relative severity of brain pathology between the three subject groups: mean signed age estimation error is 0.6 years for normal controls, 2.2 years for MCI patients, and 4.7 years for AD patients.","PeriodicalId":184204,"journal":{"name":"2008 5th IEEE International Symposium on Biomedical Imaging: From Nano to Macro","volume":"21 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2008-05-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121493434","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 : 2008-05-14DOI: 10.1109/ISBI.2008.4540942
J. Kaftan, A. Bakai, M. Das, T. Aach
Pulmonary vascular tree segmentation is the fundamental basis for different applications, such as the detection and visualization of pulmonary emboli (PE). Such an application requires an accurate and reliable segmentation of pulmonary vessels with varying diameters. We present a novel fuzzy approach to pulmonary vessel segmentation in contrast enhanced computed tomography (CT) data that considers a radius estimate of the current vessel to adapt the segmentation parameters. Hence, our method allows to capture even vessels with small diameters while suppressing leakage into surrounding structures in close proximity of vessels with large diameters. The method has been evaluated on different chest CT scans of patients referred for PE and demonstrates promising results. For quantitative validation, randomly selected sub-volumes that have been semi-automatically segmented by a medical expert have been used as reference to compare the locally adaptive method against the same method with global parameters.
{"title":"Locally adaptive fuzzy pulmonary vessel segmentation in contrast enhanced CT data","authors":"J. Kaftan, A. Bakai, M. Das, T. Aach","doi":"10.1109/ISBI.2008.4540942","DOIUrl":"https://doi.org/10.1109/ISBI.2008.4540942","url":null,"abstract":"Pulmonary vascular tree segmentation is the fundamental basis for different applications, such as the detection and visualization of pulmonary emboli (PE). Such an application requires an accurate and reliable segmentation of pulmonary vessels with varying diameters. We present a novel fuzzy approach to pulmonary vessel segmentation in contrast enhanced computed tomography (CT) data that considers a radius estimate of the current vessel to adapt the segmentation parameters. Hence, our method allows to capture even vessels with small diameters while suppressing leakage into surrounding structures in close proximity of vessels with large diameters. The method has been evaluated on different chest CT scans of patients referred for PE and demonstrates promising results. For quantitative validation, randomly selected sub-volumes that have been semi-automatically segmented by a medical expert have been used as reference to compare the locally adaptive method against the same method with global parameters.","PeriodicalId":184204,"journal":{"name":"2008 5th IEEE International Symposium on Biomedical Imaging: From Nano to Macro","volume":"30 1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2008-05-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124381362","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}
京公网安备 11010802042870号
京ICP备2023020795号-1
扫码关注我们
求助内容：
应助结果提醒方式：