Pub Date : 2017-04-01DOI: 10.1109/ISBI.2017.7950508
F. Sforazzini, Zhaolin Chen, J. Baran, J. Bradley, Alexandra Carey, N. Shah, G. Egan
Head movement is a major issue in dynamic PET imaging. A simultaneous MR-PET scanner is capable of acquiring both MR and PET data concurrently, which enables opportunities to use MR information for PET motion correction. Here we propose an MR-based method to detect head motion and to correct motion artefacts during PET image reconstruction. The method is based on co-registration of multiple MR contrasts to extract motion parameters. The motion parameters are then used to guide the Multiple Acquisition Frame (MAF) algorithm to bin the PET list-mode data into multiple frames whenever significant motion occurs. Furthermore, motion parameters are used to re-align the PET attenuation u-map to each frame prior to the image reconstruction. Finally, PET images are reconstructed for each frame and combined to produce a final image. Using both phantom and in-vivo human data, we show that this method can significantly increase image quality and reduce motion artefacts.
{"title":"MR-based attenuation map re-alignment and motion correction in simultaneous brain MR-PET imaging","authors":"F. Sforazzini, Zhaolin Chen, J. Baran, J. Bradley, Alexandra Carey, N. Shah, G. Egan","doi":"10.1109/ISBI.2017.7950508","DOIUrl":"https://doi.org/10.1109/ISBI.2017.7950508","url":null,"abstract":"Head movement is a major issue in dynamic PET imaging. A simultaneous MR-PET scanner is capable of acquiring both MR and PET data concurrently, which enables opportunities to use MR information for PET motion correction. Here we propose an MR-based method to detect head motion and to correct motion artefacts during PET image reconstruction. The method is based on co-registration of multiple MR contrasts to extract motion parameters. The motion parameters are then used to guide the Multiple Acquisition Frame (MAF) algorithm to bin the PET list-mode data into multiple frames whenever significant motion occurs. Furthermore, motion parameters are used to re-align the PET attenuation u-map to each frame prior to the image reconstruction. Finally, PET images are reconstructed for each frame and combined to produce a final image. Using both phantom and in-vivo human data, we show that this method can significantly increase image quality and reduce motion artefacts.","PeriodicalId":6547,"journal":{"name":"2017 IEEE 14th International Symposium on Biomedical Imaging (ISBI 2017)","volume":"21 1","pages":"231-234"},"PeriodicalIF":0.0,"publicationDate":"2017-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"81831772","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 : 2017-04-01DOI: 10.1109/ISBI.2017.7950499
Yani Chen, Bibo Shi, Zhewei Wang, P. Zhang, Charles D. Smith, Jundong Liu
Automated segmentation of brain structures from MR images is an important practice in many neuroimage studies. In this paper, we explore the utilization of a multi-view ensemble approach that relies on neural networks (NN) to combine multiple decision maps in achieving accurate hippocampus segmentation. Constructed under a general convolutional NN structure, our Ensemble-Net networks explore different convolution configurations to capture the complementary information residing in the multiple label probabilities produced by our U-Seg-Net (a modified U-Net) segmentation neural network. T1-weighted MRI scans and the associated Hippocampal masks of 110 healthy subjects from the ADNI project were used as the training and testing data. The combined U-Seg-Net + Ensemble-Net framework achieves over 89% Dice ratio on the test dataset.
{"title":"Hippocampus segmentation through multi-view ensemble ConvNets","authors":"Yani Chen, Bibo Shi, Zhewei Wang, P. Zhang, Charles D. Smith, Jundong Liu","doi":"10.1109/ISBI.2017.7950499","DOIUrl":"https://doi.org/10.1109/ISBI.2017.7950499","url":null,"abstract":"Automated segmentation of brain structures from MR images is an important practice in many neuroimage studies. In this paper, we explore the utilization of a multi-view ensemble approach that relies on neural networks (NN) to combine multiple decision maps in achieving accurate hippocampus segmentation. Constructed under a general convolutional NN structure, our Ensemble-Net networks explore different convolution configurations to capture the complementary information residing in the multiple label probabilities produced by our U-Seg-Net (a modified U-Net) segmentation neural network. T1-weighted MRI scans and the associated Hippocampal masks of 110 healthy subjects from the ADNI project were used as the training and testing data. The combined U-Seg-Net + Ensemble-Net framework achieves over 89% Dice ratio on the test dataset.","PeriodicalId":6547,"journal":{"name":"2017 IEEE 14th International Symposium on Biomedical Imaging (ISBI 2017)","volume":"13 1","pages":"192-196"},"PeriodicalIF":0.0,"publicationDate":"2017-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"81858650","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 : 2017-04-01DOI: 10.1109/ISBI.2017.7950480
Omer Hadad, R. Bakalo, Rami Ben-Ari, Sharbell Y. Hashoul, Guy Amit
Automatic detection and classification of lesions in medical images is a desirable goal, with numerous clinical applications. In breast imaging, multiple modalities such as X-ray, ultrasound and MRI are often used in the diagnostic workflow. Training robust classifiers for each modality is challenging due to the typically small size of the available datasets. We propose to use cross-modal transfer learning to improve the robustness of the classifiers. We demonstrate the potential of this approach on a problem of identifying masses in breast MRI images, using a network that was trained on mammography images. Comparison between cross-modal and cross-domain transfer learning showed that the former improved the classification performance, with overall accuracy of 0.93 versus 0.90, while the accuracy of de-novo training was 0.94. Using transfer learning within the medical imaging domain may help to produce standard pre-trained shared models, which can be utilized to solve a variety of specific clinical problems.
医学图像中病变的自动检测和分类是一个理想的目标，具有许多临床应用。在乳腺成像中，诊断工作流程中经常使用x射线、超声和MRI等多种方式。由于可用数据集通常规模较小，因此为每种模式训练鲁棒分类器具有挑战性。我们建议使用跨模态迁移学习来提高分类器的鲁棒性。我们展示了这种方法在乳房MRI图像中识别肿块问题上的潜力，使用了一个在乳房x光摄影图像上训练过的网络。跨模态和跨域迁移学习的比较表明，前者提高了分类性能，总体准确率为0.93 vs 0.90，而de-novo训练的准确率为0.94。在医学成像领域使用迁移学习可以帮助产生标准的预训练共享模型，可用于解决各种特定的临床问题。
{"title":"Classification of breast lesions using cross-modal deep learning","authors":"Omer Hadad, R. Bakalo, Rami Ben-Ari, Sharbell Y. Hashoul, Guy Amit","doi":"10.1109/ISBI.2017.7950480","DOIUrl":"https://doi.org/10.1109/ISBI.2017.7950480","url":null,"abstract":"Automatic detection and classification of lesions in medical images is a desirable goal, with numerous clinical applications. In breast imaging, multiple modalities such as X-ray, ultrasound and MRI are often used in the diagnostic workflow. Training robust classifiers for each modality is challenging due to the typically small size of the available datasets. We propose to use cross-modal transfer learning to improve the robustness of the classifiers. We demonstrate the potential of this approach on a problem of identifying masses in breast MRI images, using a network that was trained on mammography images. Comparison between cross-modal and cross-domain transfer learning showed that the former improved the classification performance, with overall accuracy of 0.93 versus 0.90, while the accuracy of de-novo training was 0.94. Using transfer learning within the medical imaging domain may help to produce standard pre-trained shared models, which can be utilized to solve a variety of specific clinical problems.","PeriodicalId":6547,"journal":{"name":"2017 IEEE 14th International Symposium on Biomedical Imaging (ISBI 2017)","volume":"24 1","pages":"109-112"},"PeriodicalIF":0.0,"publicationDate":"2017-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"80279749","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 : 2017-04-01DOI: 10.1109/ISBI.2017.7950709
Lucie Thiebaut Lonjaret, C. Bakhous, T. Boutelier, S. Takerkart, O. Coulon
Surface-based approaches have proven particularly relevant and reliable to study cortical functional magnetic resonance imaging (fMRI) data. However projecting fMRI volumes onto the cortical surface remains a challenging problem. Very few methods have been proposed to solve it and most of them rely on a simple interpolation. We propose here an original surface-based method based on a model representing the relationship between cortical activity and fMRI images, and a resolution through an inverse problem. This approach shows interesting perspectives for fMRI data processing as it is highly robust to noise and offers a good accuracy in terms of activations localization.
{"title":"ISA - an inverse surface-based approach for cortical fMRI data projection","authors":"Lucie Thiebaut Lonjaret, C. Bakhous, T. Boutelier, S. Takerkart, O. Coulon","doi":"10.1109/ISBI.2017.7950709","DOIUrl":"https://doi.org/10.1109/ISBI.2017.7950709","url":null,"abstract":"Surface-based approaches have proven particularly relevant and reliable to study cortical functional magnetic resonance imaging (fMRI) data. However projecting fMRI volumes onto the cortical surface remains a challenging problem. Very few methods have been proposed to solve it and most of them rely on a simple interpolation. We propose here an original surface-based method based on a model representing the relationship between cortical activity and fMRI images, and a resolution through an inverse problem. This approach shows interesting perspectives for fMRI data processing as it is highly robust to noise and offers a good accuracy in terms of activations localization.","PeriodicalId":6547,"journal":{"name":"2017 IEEE 14th International Symposium on Biomedical Imaging (ISBI 2017)","volume":"9 1","pages":"1104-1107"},"PeriodicalIF":0.0,"publicationDate":"2017-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"78645729","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 : 2017-04-01DOI: 10.1109/ISBI.2017.7950511
Philipp Flotho, A. Romero, K. Schwerdtfeger, Matthias Hulser, D. Strauss
Functional optical imaging (OI) of intrinsic signals (like blood oxygenation coupled reflection changes) and of extrinsic properties of voltage sensitive probes (like voltage-sensitive dyes (VSD)) forms a group of invasive neuroimaging techniques, that possess up to date the highest temporal and spatial resolution on a meso- to macroscopic scale.
{"title":"Energy based selective averaging approach for multi-trial optical imaging recordings","authors":"Philipp Flotho, A. Romero, K. Schwerdtfeger, Matthias Hulser, D. Strauss","doi":"10.1109/ISBI.2017.7950511","DOIUrl":"https://doi.org/10.1109/ISBI.2017.7950511","url":null,"abstract":"Functional optical imaging (OI) of intrinsic signals (like blood oxygenation coupled reflection changes) and of extrinsic properties of voltage sensitive probes (like voltage-sensitive dyes (VSD)) forms a group of invasive neuroimaging techniques, that possess up to date the highest temporal and spatial resolution on a meso- to macroscopic scale.","PeriodicalId":6547,"journal":{"name":"2017 IEEE 14th International Symposium on Biomedical Imaging (ISBI 2017)","volume":"36 1","pages":"244-247"},"PeriodicalIF":0.0,"publicationDate":"2017-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"78826896","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 : 2017-04-01DOI: 10.1109/ISBI.2017.7950646
T. Kurzendorfer, A. Brost, C. Forman, A. Maier
For electrophysiology procedures, obtaining the information of scar within the left ventricle is very important for diagnosis, therapy planning and patient prognosis. The clinical gold standard to visualize scar is late-gadolinium-enhanced-MRI (LGE-MRI). The viability assessment of the myocardium often requires the prior segmentation of the left ventricle (LV). To overcome this problem, we propose an approach for fully automatic LV segmentation in 2-D LGE-MRI. First, the LV is automatically detected using circular Hough transforms. Second, the blood pool is approximated by applying a morphological active contours approach. The refinement of the endo- and epicardial contours is performed in polar space, considering the edge information and scar distribution. The proposed method was evaluated on 26 clinical LGE-MRI data sets. This comparison resulted in a Dice coefficient of 0.85 ± 0.06 for the endocardium and 0.84 ± 0.06 for the epicardium.
{"title":"Automated left ventricle segmentation in 2-D LGE-MRI","authors":"T. Kurzendorfer, A. Brost, C. Forman, A. Maier","doi":"10.1109/ISBI.2017.7950646","DOIUrl":"https://doi.org/10.1109/ISBI.2017.7950646","url":null,"abstract":"For electrophysiology procedures, obtaining the information of scar within the left ventricle is very important for diagnosis, therapy planning and patient prognosis. The clinical gold standard to visualize scar is late-gadolinium-enhanced-MRI (LGE-MRI). The viability assessment of the myocardium often requires the prior segmentation of the left ventricle (LV). To overcome this problem, we propose an approach for fully automatic LV segmentation in 2-D LGE-MRI. First, the LV is automatically detected using circular Hough transforms. Second, the blood pool is approximated by applying a morphological active contours approach. The refinement of the endo- and epicardial contours is performed in polar space, considering the edge information and scar distribution. The proposed method was evaluated on 26 clinical LGE-MRI data sets. This comparison resulted in a Dice coefficient of 0.85 ± 0.06 for the endocardium and 0.84 ± 0.06 for the epicardium.","PeriodicalId":6547,"journal":{"name":"2017 IEEE 14th International Symposium on Biomedical Imaging (ISBI 2017)","volume":"776 1","pages":"831-834"},"PeriodicalIF":0.0,"publicationDate":"2017-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"78836226","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 : 2017-04-01DOI: 10.1109/ISBI.2017.7950572
Xiuchao Sui, Shaohua Li, Jagath Rajapakse
The human brain is organized into functionally specialized subnetworks, referred to as modules. Many methods have been employed to detect modules in the brain network, e.g. Newman's modularity and the Louvain method for community detection. However, these methods suffer from a resolution limit, and the detected number of modules is often inaccurate. In this work, we adopt Eigen Value Decomposition (EVD) on the signless Laplacian of the functional connectivity matrix to detect modules. This method is unaffected by the resolution-limit, and could identify the number of clusters more accurately. We tested the EVD method on two datasets. On a cat's cortex connectome, 5 modules were identified, agreeing with anatomical knowledge while Newman's and Louvain methods performed unstably. On the 872 fMRI scans in the Human Connectome Project, 9 modules were identified in the functional brain network, which complies well with the field knowledge.
{"title":"Detecting functional modules of the brain using eigen value decomposition of the signless Laplacian","authors":"Xiuchao Sui, Shaohua Li, Jagath Rajapakse","doi":"10.1109/ISBI.2017.7950572","DOIUrl":"https://doi.org/10.1109/ISBI.2017.7950572","url":null,"abstract":"The human brain is organized into functionally specialized subnetworks, referred to as modules. Many methods have been employed to detect modules in the brain network, e.g. Newman's modularity and the Louvain method for community detection. However, these methods suffer from a resolution limit, and the detected number of modules is often inaccurate. In this work, we adopt Eigen Value Decomposition (EVD) on the signless Laplacian of the functional connectivity matrix to detect modules. This method is unaffected by the resolution-limit, and could identify the number of clusters more accurately. We tested the EVD method on two datasets. On a cat's cortex connectome, 5 modules were identified, agreeing with anatomical knowledge while Newman's and Louvain methods performed unstably. On the 872 fMRI scans in the Human Connectome Project, 9 modules were identified in the functional brain network, which complies well with the field knowledge.","PeriodicalId":6547,"journal":{"name":"2017 IEEE 14th International Symposium on Biomedical Imaging (ISBI 2017)","volume":"13 3 1","pages":"511-514"},"PeriodicalIF":0.0,"publicationDate":"2017-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"90745283","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 : 2017-04-01DOI: 10.1109/ISBI.2017.7950580
Taweechai Ouypornkochagorn
Electrical Impedance Tomography (EIT) is an alternative way to image brain functions, in the form of conductivity distribution image, by using the boundary voltage information while a small current is injected. In head applications, due to the lack of accurate head models and the high-degree nonlinearity, the image reconstruction tends to fail. Recently, a nonlinear difference imaging approach has been proposed to mitigate modeling error. This approach, however, is based on unconstrained modeling that allows tissue conductivity values to be unrealistically negative. Consequently, substantial image artifacts are possibly conducted. In this work, two methods of constrained modeling were demonstrated they are able to substantially reduce artifacts and improve localization performance. New images of conductivity distribution of the mapped constraint domains, derived from the use of constrained modeling, are also exhibited here. The simulation result shows that the new images achieve better localization performance than those of using unconstrained modeling.
{"title":"Constrained modeling for image reconstruction in the application of Electrical Impedance Tomography to the head","authors":"Taweechai Ouypornkochagorn","doi":"10.1109/ISBI.2017.7950580","DOIUrl":"https://doi.org/10.1109/ISBI.2017.7950580","url":null,"abstract":"Electrical Impedance Tomography (EIT) is an alternative way to image brain functions, in the form of conductivity distribution image, by using the boundary voltage information while a small current is injected. In head applications, due to the lack of accurate head models and the high-degree nonlinearity, the image reconstruction tends to fail. Recently, a nonlinear difference imaging approach has been proposed to mitigate modeling error. This approach, however, is based on unconstrained modeling that allows tissue conductivity values to be unrealistically negative. Consequently, substantial image artifacts are possibly conducted. In this work, two methods of constrained modeling were demonstrated they are able to substantially reduce artifacts and improve localization performance. New images of conductivity distribution of the mapped constraint domains, derived from the use of constrained modeling, are also exhibited here. The simulation result shows that the new images achieve better localization performance than those of using unconstrained modeling.","PeriodicalId":6547,"journal":{"name":"2017 IEEE 14th International Symposium on Biomedical Imaging (ISBI 2017)","volume":"1 1","pages":"548-551"},"PeriodicalIF":0.0,"publicationDate":"2017-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"91059310","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 : 2017-04-01DOI: 10.1109/ISBI.2017.7950662
Sathiyamoorthy Selladurai, A. Thittai
In elastography, Conventional Linear Array (CLA) - based RF data acquisition can only provide good quality displacement measurements in the direction of beam propagation (axial direction). For obtaining high-precision Lateral Displacement Estimation (LDE), one of the popular methods is by interpolating A-lines in between neighboring RF A-lines. However, acquiring and utilizing the actual data from sub-pitch location will yield fundamentally better estimation. In this paper, we explore a novel method of acquiring and augmenting post-beamformed RF A-line in sub-pitch locations by electronically translating the sub-aperture by activating odd and even number of elements alternatively. We compare this approach to another recently described method where sub-pitch translations of beams were accomplished by actuator-assisted translation of the linear array transducer. The performances of the methods were studied through simulation and experiments on phantoms. The results demonstrate that these methods yield better quality LDE compared to those obtained from interpolation of RF A-lines.
{"title":"Comparison of two novel-strategies to obtain sub-pitch resolution in ultrasound elastography","authors":"Sathiyamoorthy Selladurai, A. Thittai","doi":"10.1109/ISBI.2017.7950662","DOIUrl":"https://doi.org/10.1109/ISBI.2017.7950662","url":null,"abstract":"In elastography, Conventional Linear Array (CLA) - based RF data acquisition can only provide good quality displacement measurements in the direction of beam propagation (axial direction). For obtaining high-precision Lateral Displacement Estimation (LDE), one of the popular methods is by interpolating A-lines in between neighboring RF A-lines. However, acquiring and utilizing the actual data from sub-pitch location will yield fundamentally better estimation. In this paper, we explore a novel method of acquiring and augmenting post-beamformed RF A-line in sub-pitch locations by electronically translating the sub-aperture by activating odd and even number of elements alternatively. We compare this approach to another recently described method where sub-pitch translations of beams were accomplished by actuator-assisted translation of the linear array transducer. The performances of the methods were studied through simulation and experiments on phantoms. The results demonstrate that these methods yield better quality LDE compared to those obtained from interpolation of RF A-lines.","PeriodicalId":6547,"journal":{"name":"2017 IEEE 14th International Symposium on Biomedical Imaging (ISBI 2017)","volume":"7 45 1","pages":"903-906"},"PeriodicalIF":0.0,"publicationDate":"2017-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"88033436","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 : 2017-04-01DOI: 10.1109/ISBI.2017.7950562
Nandakishor Desai, A. Seghouane, M. Palaniswami
Multisubject analysis helps to jointly analyze themedical data from multiple subjects, to make insightful inferences. Multi set canonical correlation analysis (MCCA), which extends the application of canonical correlation analysis to more than two datasets, is one such statistical technique to perform multisubject analysis. MCCA aims to compute optimal data transformations such that overall correlation of transformed datasets is maximized. But, the conventional approach is directly applicable to vector data, which requires the image data to be reshaped into vectors. Vectorization of images disturbs their spatial structure and increases computational complexity. We propose a new two dimensional MCCA approach that operates directly on the image data. Experiments are performed against fMRI data sets acquired through block-paradigm right finger tapping task.
{"title":"Multisubject fMRI data analysis via two dimensional multi-set canonical correlation analysis","authors":"Nandakishor Desai, A. Seghouane, M. Palaniswami","doi":"10.1109/ISBI.2017.7950562","DOIUrl":"https://doi.org/10.1109/ISBI.2017.7950562","url":null,"abstract":"Multisubject analysis helps to jointly analyze themedical data from multiple subjects, to make insightful inferences. Multi set canonical correlation analysis (MCCA), which extends the application of canonical correlation analysis to more than two datasets, is one such statistical technique to perform multisubject analysis. MCCA aims to compute optimal data transformations such that overall correlation of transformed datasets is maximized. But, the conventional approach is directly applicable to vector data, which requires the image data to be reshaped into vectors. Vectorization of images disturbs their spatial structure and increases computational complexity. We propose a new two dimensional MCCA approach that operates directly on the image data. Experiments are performed against fMRI data sets acquired through block-paradigm right finger tapping task.","PeriodicalId":6547,"journal":{"name":"2017 IEEE 14th International Symposium on Biomedical Imaging (ISBI 2017)","volume":"49 1","pages":"468-471"},"PeriodicalIF":0.0,"publicationDate":"2017-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"89089688","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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