| 1. |
- Qaiser, Mahmood, 1981, et al.
(författare)
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A Comparative Study of Automated Segmentation Methods for Use in a Microwave Tomography System for Imaging Intracerebral Hemorrhage in Stroke Patients
- 2015
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Ingår i: Journal of Electromagnetic Analysis and Applications. - : Scientific Research Publishing, Inc.. - 1942-0749 .- 1942-0730. ; 7, s. 152-167
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Tidskriftsartikel (refereegranskat)abstract
- Microwave technology offers the possibility for pre-hospital stroke detection as we have pre- viously demonstrated using non-imaging diagnostics. The focus in this paper is on image-based diagnostics wherein the technical and computational complexities of image reconstruction are a challenge for clinical realization. Herein we investigate whether information about a patient’s brain anatomy obtained prior to a stroke event can be used to facilitate image-based stroke diag- nostics. A priori information can be obtained by segmenting the patient’s head tissues from mag- netic resonance images. Expert manual segmentation is presently the gold standard, but it is labo- rious and subjective. A fully automatic method is thus desirable. This paper presents an evaluation of several such methods using both synthetic magnetic resonance imaging (MRI) data and real da- ta from four healthy subjects. The segmentation was performed on the full 3D MRI data, whereas the electromagnetic evaluation was performed using a 2D slice. The methods were evaluated in terms of: i) tissue classification accuracy over all tissues with respect to ground truth, ii) the accu- racy of the simulated electromagnetic wave propagation through the head, and iii) the accuracy of the image reconstruction of the hemorrhage. The segmentation accuracy was measured in terms of the degree of overlap (Dice score) with the ground truth. The electromagnetic simulation accu- racy was measured in terms of signal deviation relative to the simulation based on the ground truth. Finally, the image reconstruction accuracy was measured in terms of the Dice score, relative error of dielectric properties, and visual comparison between the true and reconstructed intrace- rebral hemorrhage. The results show that accurate segmentation of tissues (Dice score = 0.97) from the MRI data can lead to accurate image reconstruction (relative error = 0.24) for the intra- cerebral hemorrhage in the subject’s brain. They also suggest that accurate automated segmenta- tion can be used as a surrogate for manual segmentation and can facilitate the rapid diagnosis of intracerebral hemorrhage in stroke patients using a microwave imaging system.
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| 2. |
- Maier, O., et al.
(författare)
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ISLES 2015-A public evaluation benchmark for ischemic stroke lesion segmentation from multispectral MRI
- 2017
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Ingår i: Medical Image Analysis. - : Elsevier BV. - 1361-8415 .- 1361-8423. ; 35, s. 250-269
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Tidskriftsartikel (refereegranskat)abstract
- Ischemic stroke is the most common cerebrovascular disease, and its diagnosis, treatment, and study relies on non-invasive imaging. Algorithms for stroke lesion segmentation from magnetic resonance imaging (MRI) volumes are intensely researched, but the reported results are largely incomparable due to different datasets and evaluation schemes. We approached this urgent problem of comparability with the Ischemic Stroke Lesion Segmentation (ISLES) challenge organized in conjunction with the MICCAI 2015 conference. In this paper we propose a common evaluation framework, describe the publicly available datasets, and present the results of the two sub-challenges: Sub-Acute Stroke Lesion Segmentation (SISS) and Stroke Perfusion Estimation (SPES). A total of 16 research groups participated with a wide range of state-of-the-art automatic segmentation algorithms. A thorough analysis of the obtained data enables a critical evaluation of the current state-of-the-art, recommendations for further developments, and the identification of remaining challenges. The segmentation of acute perfusion lesions addressed in SPES was found to be feasible. However, algorithms applied to sub-acute lesion segmentation in SISS still lack accuracy. Overall, no algorithmic characteristic of any method was found to perform superior to the others. Instead, the characteristics of stroke lesion appearances, their evolution, and the observed challenges should be studied in detail. The annotated ISLES image datasets continue to be publicly available through an online evaluation system to serve as an ongoing benchmarking resource (www.isles-challenge.org).
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| 3. |
- Mendrik, AM, et al.
(författare)
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MRBrainS Challenge: Online Evaluation Framework for Brain Image Segmentation in 3T MRI Scans
- 2015
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Ingår i: Computational Intelligence and Neuroscience. - : Hindawi Publishing Corporation. - 1687-5265 .- 1687-5273. ; 2015
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Tidskriftsartikel (refereegranskat)abstract
- Many methods have been proposed for tissue segmentation in brain MRI scans. The multitude of methods proposed complicates the choice of one method above others. We have therefore established the MRBrainS online evaluation framework for evaluating (semi)automatic algorithms that segment gray matter (GM), white matter (WM), and cerebrospinal fluid (CSF) on 3T brain MRI scans of elderly subjects (65–80 y). Participants apply their algorithms to the provided data, after which their results are evaluated and ranked. Full manual segmentations of GM, WM, and CSF are available for all scans and used as the reference standard. Five datasets are provided for training and fifteen for testing. The evaluated methods are ranked based on their overall performance to segment GM, WM, and CSF and evaluated using three evaluation metrics (Dice, H95, and AVD) and the results are published on the MRBrainS13 website. We present the results of eleven segmentation algorithms that participated in the MRBrainS13 challenge workshop at MICCAI, where the framework was launched, and three commonly used freeware packages: FreeSurfer, FSL, and SPM. The MRBrainS evaluation framework provides an objective and direct comparison of all evaluated algorithms and can aid in selecting the best performing method for the segmentation goal at hand.
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| 4. |
- Persson, Mikael, 1959, et al.
(författare)
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Advances in Neuro Diagnostic based on Microwave Technology, Transcranial Magnetic Stimulation and EEG source localization
- 2011
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Ingår i: Asia Pacific Microwave Conference, (APMC 2011;Melbourne, VIC; 5 - 8 December 2011). - 9780858259744 ; , s. 469-472
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Konferensbidrag (refereegranskat)abstract
- Advances in neuro diagnostics based on microwave antenna system in terms of a helmet including a set of broad band patch antennas is presented. It is shown that classification algorithms can be used to detect internal bleeding in stroke patients. Transcranial magnetic stimulation has traditionally been used for brain mapping and treatment of depression. In this paper we discuss the use of the method for neuro diagnostics with the help of integrated image guidance. Surgical therapy has become an important therapeutic alternative for some patients with medically intractable epilepsy. Electroencephalography and the associated model based diagnostics as a non-invasive diagnostic tool is also discussed.
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| 5. |
- Qaiser, Mahmood, 1981, et al.
(författare)
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A fully automatic unsupervised segmentation framework for the brain tissues in MR images
- 2014
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Ingår i: Progress in Biomedical Optics and Imaging - Proceedings of SPIE. - : SPIE. - 1605-7422. - 9780819498311 ; 9038
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Konferensbidrag (refereegranskat)abstract
- This paper presents a novel fully automatic unsupervised framework for the segmentation of brain tissues in magnetic resonance (MR) images. The framework is a combination of our proposed Bayesian-based adaptive mean shift (BAMS), a priori spatial tissue probability maps and fuzzy c-means. BAMS is applied to cluster the tissues in the joint spatialintensity feature space and then a fuzzy c-means algorithm is employed with initialization by a priori spatial tissue probability maps to assign the clusters into three tissue types; white matter (WM), gray matter (GM) and cerebrospinal fluid (CSF). The proposed framework is validated on multimodal synthetic as well as on real T1-weighted MR data with varying noise characteristics and spatial intensity inhomogeneity. The performance of the proposed framework is evaluated relative to our previous method BAMS and other existing adaptive mean shift framework. Both of these are based on the mode pruning and voxel weighted k-means algorithm for classifying the clusters into WM, GM and CSF tissue. The experimental results demonstrate the robustness of the proposed framework to noise and spatial intensity inhomogeneity, and that it exhibits a higher degree of segmentation accuracy in segmenting both synthetic and real MR data compared to competing methods.
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| 6. |
- Qaiser, Mahmood, 1981, et al.
(författare)
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A novel Bayesian approach to adaptive mean shift segmentation of brain images
- 2012
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Ingår i: Proceedings - IEEE Symposium on Computer-Based Medical Systems. - 1063-7125. - 9781467320511
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Konferensbidrag (refereegranskat)abstract
- We present a novel adaptive mean shift (AMS) algorithm for the segmentation of tissues in magnetic resonance (MR) brain images. In particular we introduce a novel Bayesian approach for the estimation of the adaptive kernel bandwidth and investigate its impact on segmentation accuracy. We studied the three class problem where the brain tissues are segmented into white matter, gray matter and cerebrospinal fluid. The segmentation experiments were performed on both multi-modal simulated and real patient T1-weighted MR volumes with different noise characteristics and spatial inhomogeneities. The performance of the algorithm was evaluated relative to several competing methods using real and synthetic data. Our results demonstrate the efficacy of the proposed algorithm and that it can outperform competing methods, especially when the noise and spatial intensity inhomogeneities are high.
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| 7. |
- Qaiser, Mahmood, 1981, et al.
(författare)
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Automated MRI brain tissue segmentation based on mean shift and fuzzy c-means using a priori tissue probability maps
- 2015
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Ingår i: IRBM. - : Elsevier BV. - 1959-0318. ; 36:3, s. 185-196
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Tidskriftsartikel (refereegranskat)abstract
- This paper presents a novel fully automated unsupervised framework for the brain tissue segmentation in magnetic resonance (MR) images. The framework is a combination of Bayesian-based adaptive mean shift, a priori spatial tissue probability maps and fuzzy c-means. Mean shift is employed to cluster the tissues in the joint spatial-intensity feature space and then a fuzzy c-means is applied with initialization by a priori spatial tissue probability maps to assign the clusters into three tissue types; white matter (WM), gray matter (GM) and cerebrospinal fluid (CSF). The proposed framework is validated on a synthetic T1-weighted MR image with varying noise characteristics and spatial intensity inhomogeneity, obtained from the BrainWeb database as well as on 38 real T1-weighted MR images, obtained from the IBSR repository. The performance of the proposed framework is evaluated relative to the three widely used brain segmentation toolboxes: FAST, SPM and PVC, and the adaptive mean shift (AMS) and classical fuzzy c-means methods. The experimental results demonstrate the robustness of the proposed framework, and that it exhibits a higher degree of segmentation accuracy in segmenting both synthetic and real T1-weighted MR images compared to all competing methods.
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| 8. |
- Qaiser, Mahmood, 1981
(författare)
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Automated Patient-Specific Multi-tissue Segmentation of MR Images of the Head
- 2013
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Licentiatavhandling (övrigt vetenskapligt/konstnärligt)abstract
- The automated segmentation of magnetic resonance (MR) images of the human head is an active area of research in the field of neuroimaging. The resulting segmentation yields a patient-specific labeling of individual tissues and makes possible quantitative characterization of these tissues (e.g. in the study of Alzheimers disease and multiple sclerosis). The segmentation is also useful for assigning individual tissues conductivity or biomechanical properties for patient-specific electromagnetic and biomechanical simulations respectively. The former are of importance in applications such as EEG (electroencephalography) source localization in epilepsy patients and hyperthermia treatment planning for head and neck tumors. The latter are of interest in applications such as patient-specific motion correction and in surgical simulation.Automated and accurate segmentation of MR images is a challenging task in the field of neuroimaging because of noise, spatial intensity inhomogeneities, difficulty of MR intensity normalization and partial volume effects (a single voxel represents more than one tissue type). Consequently most of the techniques proposed to date require manual correction or intervention to achieve an accurate segmentation of the brain or whole-head. As a result they are time consuming,laborious and subjective. This thesis presents two automatic and unsupervised segmentation methods, for multi-tissue segmentation of the brain and whole-head respectively from multi-modal MR images, that are more accurate than the state-of-the-art algorithms. The brain segmentation method is based on the mean shift algorithm with a Bayesian-based adaptive bandwidth estimator. The method is called BAMS (Bayesian adaptive mean shift) and can be used to segment the brain into multiple tissue types; e.g. white matter (WM), gray matter (GM) and cerebrospinal fluid (CSF). The accuracy of BAMS was evaluated relative to that of several competing methods using both synthetic and real MRI data. The results show that it is robust to both noise and spatial intensity in- homogeneities compared to competing methods. The whole-head segmentation method is based on a hierarchical segmentation approach (HSA) incorporating the BAMS method. The segmentation performance of HSA-BAMS was evaluated relative to a reference method BET-FAST (based on the BET and FAST tools in the well-known FMRIB Software Library) and three other instantiations of the HSA, using synthetic MRI data with varying noise levels, and real MRI data. The segmentation results show the efficacy and accuracy of proposed method and that it consistently outperforms the BET-FAST reference method. HSA-BAMS was also evaluated indirectly in terms of its impact on the accuracy of EEG source localization using electromagnetic simulations based on a tissue conductivity labeling derived from the segmentation. The results demonstrate that HSA-BAMS outperforms the competing methods, and suggest that it has potential as a surrogate for manual segmentation for EEG source localization.
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| 9. |
- Qaiser, Mahmood, 1981, et al.
(författare)
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Automatic ischemic stroke lesion segmentation in multi-spectral MRI images using random forests classifier
- 2016
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Ingår i: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). - Cham : Springer International Publishing. - 1611-3349 .- 0302-9743. ; 9556, s. 266-274
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Konferensbidrag (refereegranskat)abstract
- This paper presents an automated segmentation framework for ischemic stroke lesion segmentation in multi-spectral MRI images. The framework is based on a random forests (RF), which is an ensemble learning technique that generates several classifiers and combines their results in order to make decisions. In RF, we employ several meaningful features such as intensities, entropy, gradient etc. to classify the voxels in multi-spectral MRI images. The segmentation framework is validated on both training and testing data, obtained from MICCAI ISLES-2015 SISS challenge dataset. The performance of the framework is evaluated relative to the manual segmentation (ground truth). The experimental results demonstrate the robustness of the segmentation framework, and that it achieves reasonable segmentation accuracy for segmenting the sub-acute ischemic stroke lesion in multi-spectral MRI images.
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| 10. |
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| 11. |
- Qaiser, Mahmood, 1981, et al.
(författare)
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On the Fully Automatic Construction of a Realistic Head Model for EEG Source Localization
- 2013
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Ingår i: Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS. Osaka, Japan, 3-7 July 2013. - 1557-170X. - 9781457702167 ; , s. 3331-3334
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Konferensbidrag (refereegranskat)abstract
- Accurate multi-tissue segmentation of magnetic resonance (MR) images is an essential first step in the construction of a realistic finite element head conductivity model (FEHCM) for electroencephalography (EEG) source localization. All of the segmentation approaches proposed to date for this purpose require manual intervention or correction and are thus laborious, time-consuming, and subjective. In this paper we propose and evaluate a fully automatic method based on a hierarchical segmentation approach (HSA) incorporating Bayesian-based adaptive mean-shift segmentation (BAMS). An evaluation of HSA-BAMS, as well as two reference methods, in terms of both segmentation accuracy and the source localization accuracy of the resulting FEHCM is also presented. The evaluation was performed using (i) synthetic 2D multi-modal MRI head data and synthetic EEG (generated for a prescribed source), and (ii) real 3D T1-weighted MRI head data and real EEG data (with expert determined source localization). Expert manual segmentation served as segmentation ground truth. The results show that HSA-BAMS outperforms the two reference methods and that it can be used as a surrogate for manual segmentation for the construction of a realistic FEHCM for EEG source localization.
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| 12. |
- Qaiser, Mahmood, 1981
(författare)
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Unsupervised Segmentation of Head Tissues from Multi-Modal Magnetic Resonance Images: With Application to EEG Source Localization and Stroke Detection
- 2016
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Doktorsavhandling (övrigt vetenskapligt/konstnärligt)abstract
- The automated segmentation or labeling of individual tissues in magnetic resonance (MR) images of the human head is an essential first step in several biomedical applications. The resulting segmentation yields a patient-specific labeling of individual tissues that can be used to quantitatively characterize these tissues (e.g. in the study of Alzheimers disease and multiple sclerosis) or to assign individual dielectric properties for patient-specific electromagnetic simulations (e.g. in applications such as electroencephalography source localization in epilepsy patients and microwave imaging for stroke detection). Automated and accurate segmentation of MR images is a challenging task because of the complexity and variability of the underlying anatomy and the noise and the bias field (spatial intensity inhomogeneities). Consequently, manual segmentation, including both interactive segmentation and manual correction, is largely used in clinical research. However, it is time consuming, subjective, tedious, and labor-intensive. This thesis presents new segmentation methods for both the brain and whole-head that are both automatic and accurate. It also presents empirical evaluations of these methods both directly in terms of segmentation accuracy and indirectly in terms of efficacy in electroencephalography (EEG) source localization and stroke detection. The evaluations were performed using both synthetic and real MRI data. This thesis makes four distinct contributions. The first is a novel unsupervised segmentation frame- work for segmenting MR images of the brain into three tissue types: white matter, gray matter and cerebrospinal fluid. It is a combination of Bayesian-based adaptive mean shift, incorporating an a priori tissue label probability maps, and the fuzzy c-means algorithm. The experimental results —based on both synthetic T1-weighted MR images for different noise levels and spatial intensity inhomogeneity levels, and real T1-weighted MR images —demonstrate its robustness and that it has a higher degree of segmentation accuracy than existing methods. The second is a novel automated unsupervised whole-head segmentation method for the purpose of constructing a patient-specific dielectric or biomechanical head model. The method is based on a hierarchical segmentation approach incorporating Bayesian-based adaptive mean shift. The experimental results demonstrate the efficacy of the proposed method, its robustness to noise and the bias field, and that it has a higher degree of segmentation accuracy than existing methods. The third is an evaluation of the proposed whole-head segmentation method in the context of EEG source localization. The experimental results show that the proposed method yields improved localization accuracy over the commonly used method for constructing a realistic head conductivity model for EEG source localization. The fourth is an evaluation of several existing unsupervised segmentation methods including the proposed whole-head segmentation method in the context of stroke detection using a microwave imaging system. The experimental results show that the proposed method has higher image reconstruction accuracy for intracerebral hemorrhage compared to the existing methods. The results also suggest that accurate automated segmentation can be used as a surrogate for manual segmentation to obtain accurate image reconstruction of an intracerebral hemorrhage and can assist in real time stroke detection.
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| 13. |
- Qaiser, Mahmood, 1981, et al.
(författare)
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Unsupervised Segmentation of Head Tissues from Multi-modal MR Images for EEG Source Localization
- 2015
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Ingår i: Journal of Digital Imaging. - : Springer Science and Business Media LLC. - 1618-727X .- 0897-1889. ; 28:4, s. 499-514
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Tidskriftsartikel (refereegranskat)abstract
- In this paper, we present and evaluate an automatic unsupervised segmentation method, hierarchical segmenta- tion approach (HSA)–Bayesian-based adaptive mean shift (BAMS), for use in the construction of a patient-specific head conductivity model for electroencephalography (EEG) source localization. It is based on a HSA and BAMS for segmenting the tissues from multi-modal magnetic resonance (MR) head images. The evaluation of the proposed method was done both directly in terms of segmentation accuracy and indirectly in terms of source localization accuracy. The direct evaluation was performed relative to a commonly used reference method brain extraction tool (BET)–FMRIB’s automated segmenta- tion tool (FAST) and four variants of the HSA using both synthetic data and real data from ten subjects. The synthetic data includes multiple realizations of four different noise levels and several realizations of typical noise with a 20 % bias field level. The Dice index and Hausdorff distance were used to measure the segmentation accuracy. The indirect evaluation was performed relative to the reference method BET-FAST using synthetic two-dimensional (2D) multimodal magnetic resonance (MR) data with 3 % noise and synthetic EEG (generated for a prescribed source). The source localiza- tion accuracy was determined in terms of localization error and relative error of potential. The experimental results dem- onstrate the efficacy of HSA-BAMS, its robustness to noise and the bias field, and that it provides better segmentation accuracy than the reference method and variants of the HSA. They also show that it leads to a more accurate localization accuracy than the commonly used reference method and sug- gest that it has potential as a surrogate for expert manual segmentation for the EEG source localization problem.
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| 14. |
- Shirvany, Yazdan, 1980, et al.
(författare)
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Investigation of brain tissue segmentation error and its effect on EEG source localization
- 2012
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Ingår i: Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS. - 1557-170X. ; , s. 1522-1525
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Konferensbidrag (refereegranskat)abstract
- Surgical therapy has become an important therapeutic alternative for patients with medically intractable epilepsy. Correct and anatomically precise localization of the epileptic focus, preferably with non-invasive methods, is the main goal of the pre-surgical epilepsy diagnosis to decide if resection of brain tissue is possible. For evaluating the performance of the source localization algorithms in an actual clinical situation, realistic patient-specific human head models that incorporate the heterogeneity nature of brain tissues is required. In this paper, performance of two of the most widely used software packages for brain segmentation, namely FSL and FreeSurfer has been analyzed. Then a segmented head model from a package with better performance is used to investigate the effects of brain tissue segmentation in EEG source localization.
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| 15. |
- Shirvany, Yazdan, 1980, et al.
(författare)
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Non-invasive EEG source localization using particle swarm optimization: A clinical experiment
- 2012
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Ingår i: Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS. - 1557-170X. - 9781424441198 ; , s. 6232-6235
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Konferensbidrag (refereegranskat)abstract
- One of the most important steps of pre-surgical diagnosis in patients with medically intractable epilepsy is to find the precise location of the epileptogenic foci. An Electroencephalography (EEG) is a non-invasive standard tool used at epilepsy surgery center for pre-surgical diagnosis. In this paper a modified particle swarm optimization (MPSO) method is applied to a real EEG data, i.e., a somatosensory evoked potentials (SEPs) measured from a healthy subject, to solve the EEG source localization problem. A high resolution 1 mm hexahedra finite element volume conductor model of the subject's head was generated using T1-weighted magnetic resonance imaging data. An exhaustive search pattern and the MPSO method were then applied to the peak of the averaged SEPs data. The non-invasive EEG source analysis methods localized the somatosensory cortex area where our clinical expert expected the received SEPs. The proposed inverse problem solver found the global minima with acceptable accuracy and reasonable number of iterations.
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| 16. |
- Shirvany, Yazdan, 1980, et al.
(författare)
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Particle Swarm Optimization Applied to EEG Source Localization of Somatosensory Evoked Potentials
- 2014
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Ingår i: IEEE Transactions on Neural Systems and Rehabilitation Engineering. - 1558-0210 .- 1534-4320. ; 22:1, s. 11-20
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Tidskriftsartikel (refereegranskat)abstract
- One of the most important steps in presurgical diagnosis of medically intractable epilepsy is to find the precise location of the epileptogenic foci. Electroencephalography (EEG) is a noninvasive tool commonly used at epilepsy surgery centers for presurgical diagnosis. In this paper, a modified particle swarm optimization (MPSO) method is used to solve the EEG source localization problem. The method is applied to noninvasive EEG recording of somatosensory evoked potentials (SEPs) for a healthy subject. A 1 mm hexahedra finite element volume conductor model of the subject's head was generated using T1-weighted magnetic resonance imaging data. Special consideration was made to accurately model the skull and cerebrospinal fluid. An exhaustive search pattern and the MPSO method were then applied to the peak of the averaged SEP data and both identified the same region of the somatosensory cortex as the location of the SEP source. A clinical expert independently identified the expected source location, further corroborating the source analysis methods. The MPSO converged to the global minima with significantly lower computational complexity compared to the exhaustive search method that required almost 3700 times more evaluations.
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