Pub Date : 2014-07-31DOI: 10.1109/ISBI.2014.6867949
D. Schmitter, R. Delgado-Gonzalo, G. Krueger, M. Unser
We present a new method for the atlas-free brain segmentation of proton-density-like 3D MRI images. We show how steerable filters can be efficiently combined with parametric spline surfaces to produce a fast and robust 3D brain segmentation algorithm. The novelty lies in the computation of brain edge maps through optimal steerable surface detectors which provide efficient energies for the rapid optimization of snakes. Our experimental results show the promising potential of the method for fast and accurate brain extraction.
{"title":"Atlas-free brain segmentation in 3D proton-density-like MRI images","authors":"D. Schmitter, R. Delgado-Gonzalo, G. Krueger, M. Unser","doi":"10.1109/ISBI.2014.6867949","DOIUrl":"https://doi.org/10.1109/ISBI.2014.6867949","url":null,"abstract":"We present a new method for the atlas-free brain segmentation of proton-density-like 3D MRI images. We show how steerable filters can be efficiently combined with parametric spline surfaces to produce a fast and robust 3D brain segmentation algorithm. The novelty lies in the computation of brain edge maps through optimal steerable surface detectors which provide efficient energies for the rapid optimization of snakes. Our experimental results show the promising potential of the method for fast and accurate brain extraction.","PeriodicalId":440405,"journal":{"name":"2014 IEEE 11th International Symposium on Biomedical Imaging (ISBI)","volume":"3 3","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-07-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"120847365","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 : 2014-07-31DOI: 10.1109/ISBI.2014.6868027
T. Chitiboi, A. Hennemuth, L. Tautz, M. Hüllebrand, J. Frahm, L. Linsen, H. Hahn
The recent development of a real-time magnetic resonance imaging (MRI) technique with 20 to 30 ms temporal resolution allows for imaging multiple consecutive heart cycles, without the need for breath holding or ECG synchronization. Manual analysis of the resulting image series is no longer feasible because of their length. We propose a region-based algorithm for automatically segmenting the myocardium in consecutive heart cycles based on local context and prior knowledge. The method was evaluated on ten real-time MRI series and compared to segmentations by two observers, with promising results. We show that our approach enables a multicycle analysis of the heart function robust to breathing and arrhythmia.
{"title":"Context-based segmentation and analysis of multi-cycle real-time cardiac MRI","authors":"T. Chitiboi, A. Hennemuth, L. Tautz, M. Hüllebrand, J. Frahm, L. Linsen, H. Hahn","doi":"10.1109/ISBI.2014.6868027","DOIUrl":"https://doi.org/10.1109/ISBI.2014.6868027","url":null,"abstract":"The recent development of a real-time magnetic resonance imaging (MRI) technique with 20 to 30 ms temporal resolution allows for imaging multiple consecutive heart cycles, without the need for breath holding or ECG synchronization. Manual analysis of the resulting image series is no longer feasible because of their length. We propose a region-based algorithm for automatically segmenting the myocardium in consecutive heart cycles based on local context and prior knowledge. The method was evaluated on ten real-time MRI series and compared to segmentations by two observers, with promising results. We show that our approach enables a multicycle analysis of the heart function robust to breathing and arrhythmia.","PeriodicalId":440405,"journal":{"name":"2014 IEEE 11th International Symposium on Biomedical Imaging (ISBI)","volume":"88 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-07-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121368670","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 : 2014-07-31DOI: 10.1109/ISBI.2014.6867963
F. Zhao, Xiaoxing Li, S. Biswas, R. Mullick, Paulo R. S. Mendonça, V. Vaidya
Texture analysis plays an important role in many image processing tasks. In this work, we present a texture descriptor based on the topology of excursion sets, derived from the concept of Minkowski functionals, and evaluate their usefulness in the detection of breast masses in 2D breast ultrasound images. The application includes three major stages: preprocessing, including candidate generation through computation of gradient concentration under a Fisher-Tippet noise model (in itself another contribution of the paper); texture feature extraction; and region classification using a Random Forests classifier. Performance of the proposed method is evaluated on 135 2D BUS images with 139 masses. Our method reaches 91% sensitivity with an averaged 1.19 false detections, and the proposed texture feature compares favorably against the often-used grey level co-occurrence matrices on the exact the same task.
{"title":"Topological texture-based method for mass detection in breast ultrasound image","authors":"F. Zhao, Xiaoxing Li, S. Biswas, R. Mullick, Paulo R. S. Mendonça, V. Vaidya","doi":"10.1109/ISBI.2014.6867963","DOIUrl":"https://doi.org/10.1109/ISBI.2014.6867963","url":null,"abstract":"Texture analysis plays an important role in many image processing tasks. In this work, we present a texture descriptor based on the topology of excursion sets, derived from the concept of Minkowski functionals, and evaluate their usefulness in the detection of breast masses in 2D breast ultrasound images. The application includes three major stages: preprocessing, including candidate generation through computation of gradient concentration under a Fisher-Tippet noise model (in itself another contribution of the paper); texture feature extraction; and region classification using a Random Forests classifier. Performance of the proposed method is evaluated on 135 2D BUS images with 139 masses. Our method reaches 91% sensitivity with an averaged 1.19 false detections, and the proposed texture feature compares favorably against the often-used grey level co-occurrence matrices on the exact the same task.","PeriodicalId":440405,"journal":{"name":"2014 IEEE 11th International Symposium on Biomedical Imaging (ISBI)","volume":"83 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-07-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114347452","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 : 2014-07-31DOI: 10.1109/ISBI.2014.6868022
S. Lefranc, P. Roca, M. Perrot, C. Poupon, O. Coulon, D. Bihan, L. Hertz-Pannier, J. F. Mangin, D. Rivière
Splitting the cortical surface into regions with homogeneous dMRI-based connectivity profiles is a promising but challenging topic. This paper extends the inter-subject connectivity-based cortex parcellation framework proposed by Roca [1]. In a first step, we implement the state-of-the-art algorithm with tuned parameters and, then propose a refined algorithm validated on the large high quality ARCHI database. This algorithm consists in clustering and subdividing each gyrus, in a reasonable time. Cross-validation shows that the resulting patterns are reproducible across groups. The stability is illustrated for the post-central gyrus of three different groups of subjects. Finally, the method has successfully been made robust to initial conditions: tracking type, cortical mesh characteristics and boundaries of gyri.
{"title":"Validation of consistent inter-subject connectivity-based parcellation","authors":"S. Lefranc, P. Roca, M. Perrot, C. Poupon, O. Coulon, D. Bihan, L. Hertz-Pannier, J. F. Mangin, D. Rivière","doi":"10.1109/ISBI.2014.6868022","DOIUrl":"https://doi.org/10.1109/ISBI.2014.6868022","url":null,"abstract":"Splitting the cortical surface into regions with homogeneous dMRI-based connectivity profiles is a promising but challenging topic. This paper extends the inter-subject connectivity-based cortex parcellation framework proposed by Roca [1]. In a first step, we implement the state-of-the-art algorithm with tuned parameters and, then propose a refined algorithm validated on the large high quality ARCHI database. This algorithm consists in clustering and subdividing each gyrus, in a reasonable time. Cross-validation shows that the resulting patterns are reproducible across groups. The stability is illustrated for the post-central gyrus of three different groups of subjects. Finally, the method has successfully been made robust to initial conditions: tracking type, cortical mesh characteristics and boundaries of gyri.","PeriodicalId":440405,"journal":{"name":"2014 IEEE 11th International Symposium on Biomedical Imaging (ISBI)","volume":"8 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-07-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125410944","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 : 2014-07-31DOI: 10.1109/ISBI.2014.6867996
R. Chav, T. Cresson, G. Chartrand, C. Kauffmann, G. Soulez, J. Guise
This paper reports a novel approach to 3D kidney segmentation from a single prior shape in magnetic resonance imaging (MRI) datasets. The proposed method is based on a hierarchic surface deformation algorithm, to generate a pre-personalized model, followed by an anamorphing segmentation algorithm, to extract the kidney capsule. Accuracy and precision are assessed by comparing our method over 20 kidney reconstructions segmented manually by 3 different observers on native MRI images. The experimental results show a volumetric overlap error of 6.39±2.47%, a relative volume difference of 1.87±1.39%, an average symmetric surface distance of 0.80±0.23mm, a root mean squared symmetric distance of 1.03±0.33mm and a maximum symmetric surface distance of 4.18±3.45mm. With our method, the capsules of both kidneys are segment in less than 40 seconds.
{"title":"Kidney segmentation from a single prior shape in MRI","authors":"R. Chav, T. Cresson, G. Chartrand, C. Kauffmann, G. Soulez, J. Guise","doi":"10.1109/ISBI.2014.6867996","DOIUrl":"https://doi.org/10.1109/ISBI.2014.6867996","url":null,"abstract":"This paper reports a novel approach to 3D kidney segmentation from a single prior shape in magnetic resonance imaging (MRI) datasets. The proposed method is based on a hierarchic surface deformation algorithm, to generate a pre-personalized model, followed by an anamorphing segmentation algorithm, to extract the kidney capsule. Accuracy and precision are assessed by comparing our method over 20 kidney reconstructions segmented manually by 3 different observers on native MRI images. The experimental results show a volumetric overlap error of 6.39±2.47%, a relative volume difference of 1.87±1.39%, an average symmetric surface distance of 0.80±0.23mm, a root mean squared symmetric distance of 1.03±0.33mm and a maximum symmetric surface distance of 4.18±3.45mm. With our method, the capsules of both kidneys are segment in less than 40 seconds.","PeriodicalId":440405,"journal":{"name":"2014 IEEE 11th International Symposium on Biomedical Imaging (ISBI)","volume":"227 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-07-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131464660","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 : 2014-07-31DOI: 10.1109/ISBI.2014.6867814
Bernhard Kainz, P. Voglreiter, M. Sereinigg, Iris Wiederstein-Grasser, U. Mayrhauser, Sonja Kostenbauer, M. Pollari, Rostislav Khlebnikov, M. Seise, Tuomas Alhonnoro, Yrjö Häme, D. Seider, R. Flanagan, C. Bost, Judith K. Muehl, D. O'Neill, T. Peng, S. Payne, D. Rueckert, D. Schmalstieg, M. Moche, M. Kolesnik, P. Stiegler, R. Portugaller
Data below 1 mm voxel size is getting more and more common in the clinical practice but it is still hard to obtain a consistent collection of such datasets for medical image processing research. With this paper we provide a large collection of Contrast Enhanced (CE) Computed Tomography (CT) data from porcine animal experiments and describe their acquisition procedure and peculiarities. We have acquired three CE-CT phases at the highest available scanner resolution of 57 porcine livers during induced respiratory arrest. These phases capture contrast enhanced hepatic arteries, portal venous veins and hepatic veins. Therefore, we provide scan data that allows for a highly accurate reconstruction of hepatic vessel trees. Several datasets have been acquired during Radio-Frequency Ablation (RFA) experiments. Hence, many datasets show also artificially induced hepatic lesions, which can be used for the evaluation of structure detection methods.
{"title":"High-resolution contrast enhanced multi-phase hepatic Computed Tomography data fromaporcine Radio-Frequency Ablation study","authors":"Bernhard Kainz, P. Voglreiter, M. Sereinigg, Iris Wiederstein-Grasser, U. Mayrhauser, Sonja Kostenbauer, M. Pollari, Rostislav Khlebnikov, M. Seise, Tuomas Alhonnoro, Yrjö Häme, D. Seider, R. Flanagan, C. Bost, Judith K. Muehl, D. O'Neill, T. Peng, S. Payne, D. Rueckert, D. Schmalstieg, M. Moche, M. Kolesnik, P. Stiegler, R. Portugaller","doi":"10.1109/ISBI.2014.6867814","DOIUrl":"https://doi.org/10.1109/ISBI.2014.6867814","url":null,"abstract":"Data below 1 mm voxel size is getting more and more common in the clinical practice but it is still hard to obtain a consistent collection of such datasets for medical image processing research. With this paper we provide a large collection of Contrast Enhanced (CE) Computed Tomography (CT) data from porcine animal experiments and describe their acquisition procedure and peculiarities. We have acquired three CE-CT phases at the highest available scanner resolution of 57 porcine livers during induced respiratory arrest. These phases capture contrast enhanced hepatic arteries, portal venous veins and hepatic veins. Therefore, we provide scan data that allows for a highly accurate reconstruction of hepatic vessel trees. Several datasets have been acquired during Radio-Frequency Ablation (RFA) experiments. Hence, many datasets show also artificially induced hepatic lesions, which can be used for the evaluation of structure detection methods.","PeriodicalId":440405,"journal":{"name":"2014 IEEE 11th International Symposium on Biomedical Imaging (ISBI)","volume":"20 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-07-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134294405","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}
In this paper, a global and local scheme based on graph cuts approach is proposed to segment cervical cells in images with a mix of healthy and abnormal cells. For cytoplasm segmentation, on the A* channel enhanced image, the multi-way graph cut is performed globally, which can effectively extract cytoplasm boundaries when image histograms present non-bimodal distribution. For nucleus especially abnormal nucleus segmentation, we propose to use graph cut adaptively and locally, which allows the combination of intensity, texture, boundary and region information together. Two concave-based approaches are integrated to split the touching-nuclei. On 21 cervical cell images with non-ideal imaging condition and pathology, our segmentation method achieved a 93% accuracy for cytoplasm, and a 88.4% F-measure for abnormal nuclei, both outperformed state of the art works in terms of accuracy.
{"title":"Automated segmentation of abnormal cervical cells using global and local graph cuts","authors":"Ling Zhang, Hui Kong, C. Chin, Shaoxiong Liu, Tianfu Wang, Siping Chen","doi":"10.1109/ISBI.2014.6867914","DOIUrl":"https://doi.org/10.1109/ISBI.2014.6867914","url":null,"abstract":"In this paper, a global and local scheme based on graph cuts approach is proposed to segment cervical cells in images with a mix of healthy and abnormal cells. For cytoplasm segmentation, on the A* channel enhanced image, the multi-way graph cut is performed globally, which can effectively extract cytoplasm boundaries when image histograms present non-bimodal distribution. For nucleus especially abnormal nucleus segmentation, we propose to use graph cut adaptively and locally, which allows the combination of intensity, texture, boundary and region information together. Two concave-based approaches are integrated to split the touching-nuclei. On 21 cervical cell images with non-ideal imaging condition and pathology, our segmentation method achieved a 93% accuracy for cytoplasm, and a 88.4% F-measure for abnormal nuclei, both outperformed state of the art works in terms of accuracy.","PeriodicalId":440405,"journal":{"name":"2014 IEEE 11th International Symposium on Biomedical Imaging (ISBI)","volume":"125 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-07-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132843300","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 : 2014-07-31DOI: 10.1109/ISBI.2014.6868039
K. Chen, J. Kovacevic, Ge Yang
Localization-based super-resolution techniques are revolutionizing biological research by breaking the diffraction limit of fluorescence microscopy. Each super-resolution image is reconstructed from a time series of images of randomly activated fluorophores. Here, a fundamental question is to determine the minimal imaging length so that the reconstructed image faithfully reflects the biological structures under observation. So far, proposed methods focus entirely on image resolution, which reflects localization uncertainty and fluorophore density, without taking into account the fact that images of biological structures are structured rather than random patterns. Here, we propose a different approach to determine imaging length based on direct quantification of image structural information using Gabor filters. Experimental results show that this approach is superior over approaches that only account for image-intensity distribution, confirming the importance of using structural information. In contrast to resolution-based methods, our method does not require an artificial selection of image resolution and provides a statistically rigorous strategy for determining imaging length based on image structural information.
{"title":"Structure-based determination of imaging length for super-resolution localization microscopy","authors":"K. Chen, J. Kovacevic, Ge Yang","doi":"10.1109/ISBI.2014.6868039","DOIUrl":"https://doi.org/10.1109/ISBI.2014.6868039","url":null,"abstract":"Localization-based super-resolution techniques are revolutionizing biological research by breaking the diffraction limit of fluorescence microscopy. Each super-resolution image is reconstructed from a time series of images of randomly activated fluorophores. Here, a fundamental question is to determine the minimal imaging length so that the reconstructed image faithfully reflects the biological structures under observation. So far, proposed methods focus entirely on image resolution, which reflects localization uncertainty and fluorophore density, without taking into account the fact that images of biological structures are structured rather than random patterns. Here, we propose a different approach to determine imaging length based on direct quantification of image structural information using Gabor filters. Experimental results show that this approach is superior over approaches that only account for image-intensity distribution, confirming the importance of using structural information. In contrast to resolution-based methods, our method does not require an artificial selection of image resolution and provides a statistically rigorous strategy for determining imaging length based on image structural information.","PeriodicalId":440405,"journal":{"name":"2014 IEEE 11th International Symposium on Biomedical Imaging (ISBI)","volume":"546 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-07-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131900232","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 : 2014-07-31DOI: 10.1109/ISBI.2014.6867875
Hongming Li, Yong Fan
A novel cortical surface registration method is proposed to spatially align inter-subject cortical surfaces by maximizing the similarity of their hierarchical patterns of local functional connectivity extracted from fMRI data. The cortical surface with fMRI data is characterized by functional connectivity information for each vertex of the surface to its spatial neighbors on the cortex sheet at multiple spatial scales with a hierarchical structure. Each vertex's functional connectivity information at a given scale is represented as a probability distribution of functional connectivity measures between functional signals of the vertex and its neighbors so that the functional connectivity information is independent on the vertices' spatial locations. The cortical surface registration is implemented under the spherical demons framework by matching different cortical surfaces' functional connectivity information. The experimental results for the registration of both task and resting-state fMRI data across different subjects have demonstrated that the proposed algorithm could improve the functional consistency of cortical surfaces of different subjects, and compared favorably with state-of-the-art cortical surface registration techniques.
{"title":"Spatial alignment of human cortex by matching hierarchical patterns of functional connectivity","authors":"Hongming Li, Yong Fan","doi":"10.1109/ISBI.2014.6867875","DOIUrl":"https://doi.org/10.1109/ISBI.2014.6867875","url":null,"abstract":"A novel cortical surface registration method is proposed to spatially align inter-subject cortical surfaces by maximizing the similarity of their hierarchical patterns of local functional connectivity extracted from fMRI data. The cortical surface with fMRI data is characterized by functional connectivity information for each vertex of the surface to its spatial neighbors on the cortex sheet at multiple spatial scales with a hierarchical structure. Each vertex's functional connectivity information at a given scale is represented as a probability distribution of functional connectivity measures between functional signals of the vertex and its neighbors so that the functional connectivity information is independent on the vertices' spatial locations. The cortical surface registration is implemented under the spherical demons framework by matching different cortical surfaces' functional connectivity information. The experimental results for the registration of both task and resting-state fMRI data across different subjects have demonstrated that the proposed algorithm could improve the functional consistency of cortical surfaces of different subjects, and compared favorably with state-of-the-art cortical surface registration techniques.","PeriodicalId":440405,"journal":{"name":"2014 IEEE 11th International Symposium on Biomedical Imaging (ISBI)","volume":"67 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-07-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115702087","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 : 2014-07-31DOI: 10.1109/ISBI.2014.6867968
K. Eschenburg, Julio Villalón, N. Jahanshad, T. Nir, Madelaine Daianu, Anand A. Joshi, Cassandra D. Leonardo, S. Bode, S. Bookheimer, N. Salamon, P. Thompson
Hemispherectomy is a surgical procedure for severe cases of epilepsy where an entire brain hemisphere is resected. Many patients maintain relatively normal cognitive function. Understanding how the brain's connections are organized in such cases is of interest and has not yet been explored. Using diffusion tensor imaging, we analyzed structural brain networks using various topological measures derived from brain connectivity matrices, to understand neural architecture in children with one brain hemisphere. We examined two measures derived from graph theory, global efficiency and modularity, using a k-core decomposition algorithm to compare two tractography methods and found distinctions in overall brain connectivity when comparing the two methods. While a normal control group was not available for direct comparison, this pilot study shows that it is feasible to study cortical connectivity in hemispherectomy patients.
{"title":"Analysis of structural brain connectivity in 6 cases of hemispherectomy","authors":"K. Eschenburg, Julio Villalón, N. Jahanshad, T. Nir, Madelaine Daianu, Anand A. Joshi, Cassandra D. Leonardo, S. Bode, S. Bookheimer, N. Salamon, P. Thompson","doi":"10.1109/ISBI.2014.6867968","DOIUrl":"https://doi.org/10.1109/ISBI.2014.6867968","url":null,"abstract":"Hemispherectomy is a surgical procedure for severe cases of epilepsy where an entire brain hemisphere is resected. Many patients maintain relatively normal cognitive function. Understanding how the brain's connections are organized in such cases is of interest and has not yet been explored. Using diffusion tensor imaging, we analyzed structural brain networks using various topological measures derived from brain connectivity matrices, to understand neural architecture in children with one brain hemisphere. We examined two measures derived from graph theory, global efficiency and modularity, using a k-core decomposition algorithm to compare two tractography methods and found distinctions in overall brain connectivity when comparing the two methods. While a normal control group was not available for direct comparison, this pilot study shows that it is feasible to study cortical connectivity in hemispherectomy patients.","PeriodicalId":440405,"journal":{"name":"2014 IEEE 11th International Symposium on Biomedical Imaging (ISBI)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-07-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115076413","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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