Pub Date : 2009-08-07DOI: 10.1109/ISBI.2009.5193216
V. J. Dercksen, B. Weber, D. Günther, M. Oberländer, S. Prohaska, H. Hege
We present a fast and robust method for the alignment of image stacks containing filamentous structures. Such stacks are usually obtained by physical sectioning a specimen, followed by an optical sectioning of each slice. For reconstruction, the filaments have to be traced and the sub-volumes aligned. Our algorithm takes traced filaments as input and matches their endpoints to find the optimal transform. We show that our method is able to quickly and accurately align sub-volumes containing neuronal processes, acquired using brightfield microscopy. Our method also makes it possible to align traced microtubuli, obtained from electron tomography data, which are extremely difficult to align manually.
{"title":"Automatic alignment of stacks of filament data","authors":"V. J. Dercksen, B. Weber, D. Günther, M. Oberländer, S. Prohaska, H. Hege","doi":"10.1109/ISBI.2009.5193216","DOIUrl":"https://doi.org/10.1109/ISBI.2009.5193216","url":null,"abstract":"We present a fast and robust method for the alignment of image stacks containing filamentous structures. Such stacks are usually obtained by physical sectioning a specimen, followed by an optical sectioning of each slice. For reconstruction, the filaments have to be traced and the sub-volumes aligned. Our algorithm takes traced filaments as input and matches their endpoints to find the optimal transform. We show that our method is able to quickly and accurately align sub-volumes containing neuronal processes, acquired using brightfield microscopy. Our method also makes it possible to align traced microtubuli, obtained from electron tomography data, which are extremely difficult to align manually.","PeriodicalId":272938,"journal":{"name":"2009 IEEE International Symposium on Biomedical Imaging: From Nano to Macro","volume":"17 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2009-08-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130467060","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 : 2009-06-28DOI: 10.1109/ISBI.2009.5193104
Sheng-Fang Huang, H. Chao, Cheng-Chin Hsu, Shan-Fong Yang, P. Kao
Tc-99m MDP whole body bone scan using single photon emission computed tomography (SPECT) is an important and general method to investigate the spreading of malignant tumors. However, it is time-consuming for doctors to perform three-dimensional (3D) assessment using SPECT images. Therefore, a computer-aided diagnosis (CAD) system is required to identify suspicious locations of bone abnormalities. In this study, we developed a 3D-based segmentation method and a quantitative scheme to detect the findings of possible abnormalities. In this method, we designed a new data structure called bone graph that characterizes scanned images as graph, where by tracking this graph, we can extract the morphological features from the entire skeleton. The proposed scheme automatically extracts the skeletal structure of human spine, and can be adopted to assist nuclear medicine physicians to identify the potential locations of bone lesions.
{"title":"A computer-aided diagnosis system for whole body bone scan using single photon emission computed tomography","authors":"Sheng-Fang Huang, H. Chao, Cheng-Chin Hsu, Shan-Fong Yang, P. Kao","doi":"10.1109/ISBI.2009.5193104","DOIUrl":"https://doi.org/10.1109/ISBI.2009.5193104","url":null,"abstract":"Tc-99m MDP whole body bone scan using single photon emission computed tomography (SPECT) is an important and general method to investigate the spreading of malignant tumors. However, it is time-consuming for doctors to perform three-dimensional (3D) assessment using SPECT images. Therefore, a computer-aided diagnosis (CAD) system is required to identify suspicious locations of bone abnormalities. In this study, we developed a 3D-based segmentation method and a quantitative scheme to detect the findings of possible abnormalities. In this method, we designed a new data structure called bone graph that characterizes scanned images as graph, where by tracking this graph, we can extract the morphological features from the entire skeleton. The proposed scheme automatically extracts the skeletal structure of human spine, and can be adopted to assist nuclear medicine physicians to identify the potential locations of bone lesions.","PeriodicalId":272938,"journal":{"name":"2009 IEEE International Symposium on Biomedical Imaging: From Nano to Macro","volume":"45 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2009-06-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"117155159","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 : 2009-06-28DOI: 10.1109/ISBI.2009.5193108
G. Kande, T. Savithri, P. Subbaiah, M. Tagore
This paper presents an efficient approach for automatic detection of red lesions in ocular fundus images. The approach uses the intensity information from red and green channels of the same retinal image to correct non-uniform illumination in color fundus images. Matched filtering is utilized to enhance the contrast of red lesions against the background. The enhanced red lesions are then segmented by employing relative entropy based thresholding which can well maintain the spatial structure of the red lesion segments. Then morphological top-hat transformation is used to suppress the enhanced vasculature. SVIvIs are used to classify the candidate red lesions from other dark segments. Experimental evaluation of the proposed approach demonstrates superior performance over other red lesion detection algorithms recently reported in the literature.
{"title":"Detection of red lesions in digital fundus images","authors":"G. Kande, T. Savithri, P. Subbaiah, M. Tagore","doi":"10.1109/ISBI.2009.5193108","DOIUrl":"https://doi.org/10.1109/ISBI.2009.5193108","url":null,"abstract":"This paper presents an efficient approach for automatic detection of red lesions in ocular fundus images. The approach uses the intensity information from red and green channels of the same retinal image to correct non-uniform illumination in color fundus images. Matched filtering is utilized to enhance the contrast of red lesions against the background. The enhanced red lesions are then segmented by employing relative entropy based thresholding which can well maintain the spatial structure of the red lesion segments. Then morphological top-hat transformation is used to suppress the enhanced vasculature. SVIvIs are used to classify the candidate red lesions from other dark segments. Experimental evaluation of the proposed approach demonstrates superior performance over other red lesion detection algorithms recently reported in the literature.","PeriodicalId":272938,"journal":{"name":"2009 IEEE International Symposium on Biomedical Imaging: From Nano to Macro","volume":"55 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2009-06-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125117109","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 : 2009-06-28DOI: 10.1109/ISBI.2009.5193064
R. Kunz, D. Haworth, D. Porzio, A. Kriete
A semi-automated end-to-end medical image through Computational Fluid Dynamics (CFD) analysis toolkit has been developed, with the ultimate goal of providing clinical-time-scale (hours) diagnostic information for respiratory disease and injury assessment. A software system is in place that proceeds from a standard clinical image format, through lobe and upper bronchi segmentation, upper airway “thinning”, airway generation partitioning, and truncation, lower bronchiole lobe volume filling, octree-based unstructured mesh generation for the upper airways, quasi-one-dimensional geometric modeling for the lower airways, and CFD analysis of respiration. Each of these components is presented.
{"title":"Progress towards a medical image through CFD analysis toolkit for respiratory function assessment on a clinical time scale","authors":"R. Kunz, D. Haworth, D. Porzio, A. Kriete","doi":"10.1109/ISBI.2009.5193064","DOIUrl":"https://doi.org/10.1109/ISBI.2009.5193064","url":null,"abstract":"A semi-automated end-to-end medical image through Computational Fluid Dynamics (CFD) analysis toolkit has been developed, with the ultimate goal of providing clinical-time-scale (hours) diagnostic information for respiratory disease and injury assessment. A software system is in place that proceeds from a standard clinical image format, through lobe and upper bronchi segmentation, upper airway “thinning”, airway generation partitioning, and truncation, lower bronchiole lobe volume filling, octree-based unstructured mesh generation for the upper airways, quasi-one-dimensional geometric modeling for the lower airways, and CFD analysis of respiration. Each of these components is presented.","PeriodicalId":272938,"journal":{"name":"2009 IEEE International Symposium on Biomedical Imaging: From Nano to Macro","volume":"65 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2009-06-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123576873","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 : 2009-06-28DOI: 10.1109/ISBI.2009.5193277
Maximilian Baust, S. Demirci, Nassir Navab
Being performed under extensive radiation exposure, endovascular stent graft placements would greatly benefit from a reliable navigation solution. A successful implementation of such a system requires an accurate 2D–3D registration. Since the stent graft is only visible in the radiograph, registration algorithms can easily be attracted to wrong structures. In this paper, we address this problem by presenting a fast algorithm for removing the stent graft which meets real-time constraints. Based on Poisson editing, our method is easy to implement and extremely user-friendly as it requires neither parameter adjustment nor precise presegmentation. Moreover, we prove the significance of our algorithm by a realistic ground truth study.
{"title":"Stent graft removal for improving 2D–3D registration","authors":"Maximilian Baust, S. Demirci, Nassir Navab","doi":"10.1109/ISBI.2009.5193277","DOIUrl":"https://doi.org/10.1109/ISBI.2009.5193277","url":null,"abstract":"Being performed under extensive radiation exposure, endovascular stent graft placements would greatly benefit from a reliable navigation solution. A successful implementation of such a system requires an accurate 2D–3D registration. Since the stent graft is only visible in the radiograph, registration algorithms can easily be attracted to wrong structures. In this paper, we address this problem by presenting a fast algorithm for removing the stent graft which meets real-time constraints. Based on Poisson editing, our method is easy to implement and extremely user-friendly as it requires neither parameter adjustment nor precise presegmentation. Moreover, we prove the significance of our algorithm by a realistic ground truth study.","PeriodicalId":272938,"journal":{"name":"2009 IEEE International Symposium on Biomedical Imaging: From Nano to Macro","volume":"12 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2009-06-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125370137","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 : 2009-06-28DOI: 10.1109/ISBI.2009.5193324
Mukund Desai, R. Mangoubi, P. Sammak
We present a methodology for characterizing small size heterogeneous textures that are hard to analyze in general due to the paucity of pixels and textural heterogeneity. The methodology overcomes the limitation for a large class of heterogeneous textures that exhibit onion layer type textural variation, where we may assume that within a layer the behavior is homogeneous, but may vary from layer to layer. The shape of the onion layers is data dependent; radial symmetry is not required. We use an energy functional approach for simultaneous smoothing and segmentation that relies on two key innovations: a matrix edge field, and adaptive weighting of the measurements relative to the smoothing process model. The matrix edge function adaptively and implicitly modulates the shape, size, and orientation of smoothing neighborhoods over different regions of the texture. It thus provides directional information on the texture that is not available in the more conventional scalar edge field based approaches. The adaptive measurement weighting varies the weighting between the measurements at each pixel. Image based analysis of human embryonic stem cells is the motivating application for this new approach, and we show how the features extracted using this approach can be used to automate the classification of pluripotent vs. differentiated stem cell nuclei based on confocal images of fluorescent GFP-labeled chromatin.
{"title":"Noise adaptive matrix edge field analysis of small sized heterogeneous onion layered textures for characterizing human embryonic stem cell nuclei","authors":"Mukund Desai, R. Mangoubi, P. Sammak","doi":"10.1109/ISBI.2009.5193324","DOIUrl":"https://doi.org/10.1109/ISBI.2009.5193324","url":null,"abstract":"We present a methodology for characterizing small size heterogeneous textures that are hard to analyze in general due to the paucity of pixels and textural heterogeneity. The methodology overcomes the limitation for a large class of heterogeneous textures that exhibit onion layer type textural variation, where we may assume that within a layer the behavior is homogeneous, but may vary from layer to layer. The shape of the onion layers is data dependent; radial symmetry is not required. We use an energy functional approach for simultaneous smoothing and segmentation that relies on two key innovations: a matrix edge field, and adaptive weighting of the measurements relative to the smoothing process model. The matrix edge function adaptively and implicitly modulates the shape, size, and orientation of smoothing neighborhoods over different regions of the texture. It thus provides directional information on the texture that is not available in the more conventional scalar edge field based approaches. The adaptive measurement weighting varies the weighting between the measurements at each pixel. Image based analysis of human embryonic stem cells is the motivating application for this new approach, and we show how the features extracted using this approach can be used to automate the classification of pluripotent vs. differentiated stem cell nuclei based on confocal images of fluorescent GFP-labeled chromatin.","PeriodicalId":272938,"journal":{"name":"2009 IEEE International Symposium on Biomedical Imaging: From Nano to Macro","volume":"31 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2009-06-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126758337","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 : 2009-06-28DOI: 10.1109/ISBI.2009.5193182
Danijela Vukadinovic, T. Walsum, S. Rozie, T. Weert, R. Manniesing, A. Lugt, W. Niessen
A novel, slice-based, semi-automatic method for plaque segmentation and quantification in CTA of carotid arteries is introduced. The method starts with semi-automatic, levelset based, lumen segmentation initialized with three points. Pixel based GentleBoost classification is used to segment the inner and outer vessel wall region using distance from the lumen, intensity and Gaussian derivatives as features. 3D calcified regions located within the vessel wall are segmented using a similar set of features and the same classification method. Subsequently, an ellipse-shaped deformable model is fitted using the inner-outer vessel wall and calcium classification, and plaque components within the wall are characterized using HU ranges. The method is quantitatively evaluated on 5 carotid arteries. Vessel and plaque segmentation are compared to the interobserver variability. Furthermore, correlation of slice-based plaque component quantification with the ground truth values is determined. The accuracy of our method is comparable to the interobserver variability.
{"title":"Carotid artery segmentation and plaque quantification in CTA","authors":"Danijela Vukadinovic, T. Walsum, S. Rozie, T. Weert, R. Manniesing, A. Lugt, W. Niessen","doi":"10.1109/ISBI.2009.5193182","DOIUrl":"https://doi.org/10.1109/ISBI.2009.5193182","url":null,"abstract":"A novel, slice-based, semi-automatic method for plaque segmentation and quantification in CTA of carotid arteries is introduced. The method starts with semi-automatic, levelset based, lumen segmentation initialized with three points. Pixel based GentleBoost classification is used to segment the inner and outer vessel wall region using distance from the lumen, intensity and Gaussian derivatives as features. 3D calcified regions located within the vessel wall are segmented using a similar set of features and the same classification method. Subsequently, an ellipse-shaped deformable model is fitted using the inner-outer vessel wall and calcium classification, and plaque components within the wall are characterized using HU ranges. The method is quantitatively evaluated on 5 carotid arteries. Vessel and plaque segmentation are compared to the interobserver variability. Furthermore, correlation of slice-based plaque component quantification with the ground truth values is determined. The accuracy of our method is comparable to the interobserver variability.","PeriodicalId":272938,"journal":{"name":"2009 IEEE International Symposium on Biomedical Imaging: From Nano to Macro","volume":"62 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2009-06-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126767696","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 : 2009-06-28DOI: 10.1109/ISBI.2009.5193116
H. Fayad, T. Pan, C. Roux, C. Rest, O. Pradier, D. Visvikis
Modeling of respiratory motion is very important for the efficacy of radiation therapy (RT) which is used in the treatment of cancer in the thorax and the abdomen. Having such a model is a key point to deliver, under breathing induced motion, less dose to the normal healthy tissues and higher dose to the tumor. Many methods have been developed to reduce the respiratory motion induced errors. While 4D CT based methods produce a number of separate frames at different positions in the respiratory cycle, a continuous motion model will be more efficient for radiation therapy. In this paper, we describe an approach based on the creation of a continuous patient specific model that takes into account respiratory signal irregularities and reproduces respiration-induced organ motion. This model has been validated on three patients. Our results show that including both phase and amplitude for the model reconstruction leads to higher accuracy compared to the use of only one of these two parameters.
{"title":"A 2D-spline patient specific model for use in radiation therapy","authors":"H. Fayad, T. Pan, C. Roux, C. Rest, O. Pradier, D. Visvikis","doi":"10.1109/ISBI.2009.5193116","DOIUrl":"https://doi.org/10.1109/ISBI.2009.5193116","url":null,"abstract":"Modeling of respiratory motion is very important for the efficacy of radiation therapy (RT) which is used in the treatment of cancer in the thorax and the abdomen. Having such a model is a key point to deliver, under breathing induced motion, less dose to the normal healthy tissues and higher dose to the tumor. Many methods have been developed to reduce the respiratory motion induced errors. While 4D CT based methods produce a number of separate frames at different positions in the respiratory cycle, a continuous motion model will be more efficient for radiation therapy. In this paper, we describe an approach based on the creation of a continuous patient specific model that takes into account respiratory signal irregularities and reproduces respiration-induced organ motion. This model has been validated on three patients. Our results show that including both phase and amplitude for the model reconstruction leads to higher accuracy compared to the use of only one of these two parameters.","PeriodicalId":272938,"journal":{"name":"2009 IEEE International Symposium on Biomedical Imaging: From Nano to Macro","volume":"12 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2009-06-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116442569","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 : 2009-06-28DOI: 10.1109/ISBI.2009.5193051
Synho Do, Sanghee Cho, W. C. Karl, M. Kalra, T. Brady, H. Pien
Cardiac imaging represents one of the most challenging imaging problems, requiring high spatial and temporal resolutions along with good tissue contrast. One of the newest clinical cardiac CT scanners incorporates two source-detector pairs in order to improve the temporal resolution by two-fold. To achieve the highest spatial resolution, reconstructions using iterative techniques may be desired. Yet the complexity of the dual-source geometry makes accurate system modeling a challenge. In this paper, we present a model-based iterative reconstruction algorithm for the dual-source CT. We demonstrate, using a total variation formulation, the results of our reconstructions. To accelerate the processing and enhance the quality of the result, we also incorporate a simplified detector response function in the forward projector. A segment of heavily-calcified coronary artery is used to demonstrate the spatial and temporal resolution of this approach with the dual-source system.
{"title":"Accurate model-based high resolution cardiac image reconstruction in dual source CT","authors":"Synho Do, Sanghee Cho, W. C. Karl, M. Kalra, T. Brady, H. Pien","doi":"10.1109/ISBI.2009.5193051","DOIUrl":"https://doi.org/10.1109/ISBI.2009.5193051","url":null,"abstract":"Cardiac imaging represents one of the most challenging imaging problems, requiring high spatial and temporal resolutions along with good tissue contrast. One of the newest clinical cardiac CT scanners incorporates two source-detector pairs in order to improve the temporal resolution by two-fold. To achieve the highest spatial resolution, reconstructions using iterative techniques may be desired. Yet the complexity of the dual-source geometry makes accurate system modeling a challenge. In this paper, we present a model-based iterative reconstruction algorithm for the dual-source CT. We demonstrate, using a total variation formulation, the results of our reconstructions. To accelerate the processing and enhance the quality of the result, we also incorporate a simplified detector response function in the forward projector. A segment of heavily-calcified coronary artery is used to demonstrate the spatial and temporal resolution of this approach with the dual-source system.","PeriodicalId":272938,"journal":{"name":"2009 IEEE International Symposium on Biomedical Imaging: From Nano to Macro","volume":"68 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2009-06-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122598645","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 : 2009-06-28DOI: 10.1109/ISBI.2009.5193289
G. Tsechpenakis, J. Eugenín, J. Nicholls, K. J. Muller
Optical recording of the activity of hundreds of individual neurons simultaneously within the functioning brain is now possible with calcium sensitive dyes. This offers a major advance over the limitations of single-unit recording with arrays of microelectrodes, or with functional MRI. However, the analysis of optical activity to understand neuronal interactions and circuitry underlying physiological functions requires new computational approaches. Recently it has been possible to record optically from the distributed population of neurons in the brain stem generating the respiratory rhythm, breath by breath, using the compact brain stem and spinal cord preparation of the fetal mouse stained in vitro with calcium-sensitive dye. The simultaneous electrical activity of phrenic motoneurons that innervate the diaphragm measures the timing of inspiratory breaths. In the present work, fluorescence micrographs taken at 4–100Hz over 20–40sec have been analyzed with the simultaneously recorded electrical signal from the phrenic nerve, in a Conditional Random Field framework. This computational analysis will be a useful tool for understanding the cellular circuitry in the living brain controlling fundamental physiological processes.
{"title":"Analysis of nerve activity and optical signals from mouse brain stem to identify cells generating respiratory rhythms","authors":"G. Tsechpenakis, J. Eugenín, J. Nicholls, K. J. Muller","doi":"10.1109/ISBI.2009.5193289","DOIUrl":"https://doi.org/10.1109/ISBI.2009.5193289","url":null,"abstract":"Optical recording of the activity of hundreds of individual neurons simultaneously within the functioning brain is now possible with calcium sensitive dyes. This offers a major advance over the limitations of single-unit recording with arrays of microelectrodes, or with functional MRI. However, the analysis of optical activity to understand neuronal interactions and circuitry underlying physiological functions requires new computational approaches. Recently it has been possible to record optically from the distributed population of neurons in the brain stem generating the respiratory rhythm, breath by breath, using the compact brain stem and spinal cord preparation of the fetal mouse stained in vitro with calcium-sensitive dye. The simultaneous electrical activity of phrenic motoneurons that innervate the diaphragm measures the timing of inspiratory breaths. In the present work, fluorescence micrographs taken at 4–100Hz over 20–40sec have been analyzed with the simultaneously recorded electrical signal from the phrenic nerve, in a Conditional Random Field framework. This computational analysis will be a useful tool for understanding the cellular circuitry in the living brain controlling fundamental physiological processes.","PeriodicalId":272938,"journal":{"name":"2009 IEEE International Symposium on Biomedical Imaging: From Nano to Macro","volume":"4 2","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2009-06-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114044743","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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